{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#This notebook is designed to be converted to a HTML slide show\n",
    "#To do this in the command prompt type (in the folder containing the notebook): \n",
    "#       jupyter nbconvert *.ipynb --to slides"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# **Simulating Chemical Reaction Networks with Bioscrape**\n",
    "## BE 240 April 6th 2020\n",
    "### _William Poole_\n",
    "\n",
    "**Overview:** In this tutorial, we will review deterministic and stochastic mass action chemical reaction networks (CRNs) and how to simulate them using [BioSCRAPE](https://github.com/ananswam/bioscrape)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What is a CRN?\n",
    "A CRN is a widely established model of chemistry and biochemistry.\n",
    "* A set of species $S$\n",
    "* A set of reactions $R$ interconvert species $I_r$ to $O_r$\n",
    "\n",
    "\\begin{align}\n",
    "\\\\\n",
    "I \\xrightarrow[]{\\rho(s)} O\n",
    "\\\\\n",
    "\\end{align}\n",
    "\n",
    " * $I$ and $O$ are multisets of species $S$. \n",
    " * $\\rho(s): S \\to \\mathbb{R}$ is a function that determines how fast the reaction occurs.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "### Example\n",
    "\\begin{equation}\n",
    "\\emptyset  \\underset{1}{\\overset{100}\\rightleftharpoons}  2 A\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:11: UserWarning: The following species are uninitialized and their value has been defaulted to 0: A, \n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:32: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Concentration \\n (Stochastic Counts)')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the example - code not shown in lecture\n",
    "\n",
    "#Using Bioscrape: Basic Imports\n",
    "from bioscrape.types import Model\n",
    "from bioscrape.simulator import py_simulate_model\n",
    "#For arrays and plotting\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "\n",
    "\n",
    "CRN = Model(species = [\"A\"], reactions = [([], [\"A\", \"A\"], \"massaction\", {\"k\":100}), ([\"A\", \"A\"], [], \"massaction\", {\"k\":1})])\n",
    "\n",
    "\n",
    "T = 60\n",
    "N = 500\n",
    "tmax = .3\n",
    "timepoints = np.linspace(0, tmax, T)\n",
    "HM = np.zeros((T, 25)) #Heatmap for storing the probability distirbution P[t, A]\n",
    "\n",
    "#Run N simulations\n",
    "for i in range(N):\n",
    "    #Simulate model stochastically\n",
    "    R = py_simulate_model(timepoints = timepoints, Model = CRN, stochastic = True)\n",
    "    \n",
    "    #Add to HM dist\n",
    "    for t in range(T):\n",
    "        if R[\"A\"][t] < 25: #Truncate if the value is out of the heatmap (very rare)\n",
    "            HM[t, int(R[\"A\"][t])]+=1/N #Renomralize per simulation\n",
    "    \n",
    "\n",
    "    plt.figure(\"fig stoch\", figsize = (10, 4))\n",
    "    plt.subplot(121)\n",
    "    plt.plot(timepoints, R[\"A\"], color = (0, i/N, 1-i/N), alpha = np.exp(-i/2))\n",
    "\n",
    "    plt.figure(\"fig det\", figsize = (10, 4))\n",
    "    plt.plot(timepoints, R[\"A\"], color = (0, i/N, 1-i/N), alpha = .6*np.exp(-i/5))\n",
    "\n",
    "plt.figure(\"fig stoch\")\n",
    "plt.title(\"Stochastic Trajectories: $\\emptyset \\leftrightarrow 2A$\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.ylim(0, 25)\n",
    "plt.subplot(122)\n",
    "plt.title(\"Probability Distribution $\\subseteq$ Integer Lattice\")\n",
    "cb = plt.pcolor(HM.T)\n",
    "plt.colorbar(cb)\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"P[A]\")\n",
    "plt.xticks([10*i for i in range(int(T/10)+1)], [str(round(i/T*tmax*10, 3)) for i in range(int(T/10)+1)])\n",
    "\n",
    "plt.figure(\"fig det\")\n",
    "R = py_simulate_model(timepoints = timepoints, Model = CRN, stochastic = False) #Deterministic Simulation\n",
    "plt.plot(timepoints, R[\"A\"], color = 'black', lw = 3)\n",
    "plt.title('Concentration Trajectories are an \"Average\" of Count Trajectories')\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Concentration \\n (Stochastic Counts)\")"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# CRNs can be interpreted stochastically\n",
    "\n",
    "**Stochastic Semantics** considers the probability of counts of molecules:\n",
    "\n",
    "\n",
    "\\begin{align}\n",
    "\\frac{d \\mathbb{P}[s]}{dt} = \\sum_r \\mathbb{P}[s + \\Delta_r] \\rho_r(s + \\Delta_r) - \\mathbb{P}[s]\\rho_r(s)\n",
    "\\end{align}\n",
    "\n",
    "* This equation is the chemical master equation and can be simulated via the [Gillespie Algorithm](http://cctbio.ece.umn.edu/wiki/images/7/78/Gillespie-Daniel-T_Stochastic_Simulation_of_Chemical_Kinetics.pdf) as a Markov jump process\n",
    "* $s_i = \\textrm{count} (S_i)$\n",
    "* $\\rho_r(s)$ is called the \"propensity\" of reaction $r$\n",
    "* $\\Delta_r = O_r - I_r$ is the stochiometric matrix\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## CRNs can be interpreted deterministically\n",
    "\n",
    "**Deterministic Semantics** considers the concentrations of molecules:\n",
    "\n",
    "\\begin{equation}\n",
    "\\lim_{s_i, V \\to \\infty} \\textrm{ s.t. } \\frac{s_i}{V} = [S_i] \\quad \\quad \\quad \\frac{d[S]}{dt} = \\sum_r \\Delta_r \\rho_r([S])\n",
    "\\end{equation}\n",
    "\n",
    "* This limit is subtle and is not the same as the expected value.\n",
    "* The dynamics are an ordinary differential equation (ODE):\n",
    "* $\\rho_r([S])$ is called the \"rate function\" of reaction $r$\n",
    "\n",
    "![image.png](attachment:image.png)\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Deterministic Law of Mass Action\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "_Peter Waage (left) and  Cato Maximilian Guldberg (right) developed the \"Laws of Mass Action\" in the late 1800's. You probably learned these laws in your introductory undergraduate chemistry course._\n",
    "  \n",
    "    \n",
    "For a reaction $A + B \\underset{k^-}{\\overset{k^+}\\rightleftharpoons} C + D$, at equilibrium:\n",
    "  \n",
    "\n",
    "    \n",
    "\\begin{align}\n",
    "k^+ [A][B] = k^-[C][D]\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## The Deterministic Mass Action Rate Equation:\n",
    "\n",
    "\\begin{align}\n",
    "\\\\\n",
    "\\rho_r([S]) =k_r \\prod_i [S_i]^{I_i}\n",
    "\\end{align}\n",
    "\n",
    "\n",
    "Some Examples:\n",
    "\\begin{align}\n",
    "\\\\\n",
    "\\emptyset &\\xrightarrow{k} A \\quad \\quad &&\\rho([S]) = k\n",
    "\\\\ \\\\\n",
    "A &\\xrightarrow{k} B \\quad \\quad &&\\rho([S]) = k [A]\n",
    "\\\\ \\\\\n",
    "3A + B &\\xrightarrow{k} C + D \\quad \\quad &&\\rho([S]) = k [A]^3 [B]\n",
    "\\end{align}"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Stochastic Massaction Propensities Arrise from Statistical Physics\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "_Ludwig Eduard Boltzmann invented kinetic theory, which is a foundation of stochastic chemical reaction network theory._\n",
    "\n",
    "Consider the reaction:\n",
    "\\begin{equation}\n",
    "A + B \\xrightarrow[]{k}C + D \\quad \\quad \\quad \\quad \\rho(A, B) = \\frac{1}{V} k A B\n",
    "\\end{equation}\n",
    "\n",
    "For this reaction to occur, $A$ and $B$ must collide in solution. The rate constant $k$ is defined as the rate each molecule of $A$ has of colliding with each molecule of $B$ and reacting to form $C + D$ per unit concentration. Recalling that concentration is $[\\cdot] = \\frac{\\textrm{count }(\\cdot)}{V}$, we have the divide by $V$ multiply by the counts of each species.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## The General Stochastic Massaction Propensities Function:\n",
    "\n",
    "\n",
    "\n",
    "\\begin{align}\n",
    "\\\\\n",
    "I &\\xrightarrow[]{k_r} O \\quad \\quad &&\\rho(s) = \\frac{k_r}{V^{|I|-1}} \\prod_i \\frac{s_i!}{(s_i - I_i)!}\n",
    "\\end{align}\n",
    "\n",
    "\n",
    "Some Examples:\n",
    "\n",
    "\n",
    "\\begin{align}\n",
    "\\\\\n",
    "\\emptyset &\\xrightarrow{k} A \\quad \\quad &&\\rho(s) = k V \\\\ \\\\\n",
    "A &\\xrightarrow{k} B \\quad \\quad &&\\rho(s) = k A \\\\ \\\\\n",
    "3A + B &\\xrightarrow{k} C + D \\quad \\quad &&\\rho(s) = \\frac{k}{V^3} A(A-1)(A-2) B\n",
    "\\end{align}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The Well Mixed Assumption\n",
    "\n",
    "Both the deterministic and stochastic mass-action functions assume that the solution is \"well mixed\" meaning if molecules $A$ and $B$ just reacted, they are not more likely to react again compared to other reactions with molecules $C$, $D$, etc. In other words, diffusion is fast compared to to the rates of chemical reactions.\n",
    "\n",
    "### Examples of well mixed reactions:\n",
    "* Test-tube chemical reactions like PCR\n",
    "* Chemical reactions in the cytoplasm in a cell\n",
    "\n",
    "### Examples of reactions which are not necessarily well mixed:\n",
    "* Interactions between proteins bound to the same piece of DNA\n",
    "* Interactions occuring occuring in multiple cellular organelles\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# When to use stochastic simulation versus deterministic?\n",
    "\n",
    "### Stochastic:\n",
    "* Small Copy number of molecules ($n<20$)\n",
    "* Investigating noise and variance\n",
    "* Single cell data (microscopy, flow cytometry)\n",
    "\n",
    "### Deterministic:\n",
    "* Large copy number ($n>100$)\n",
    "* Investigating bulk or average behavior (plate reader data, populations of cells)\n",
    "* When analytic and/or numerical tractability are important\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What Is Bioscrape?\n",
    "\n",
    "### Biological Circuit Stochastic Simulation of Single Cell Reactions and Parameter Estimation\n",
    "\n",
    "Bioscrape is python software to simulate CRNs deterministically or stochastically\n",
    "* Also does parameter inference and simulates single cells....to be discussed in future lectures\n",
    "\n",
    "\n",
    "_Biocrape is designed to be fast and modable with a python API so it can be connected into data/modelling pipelines_"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Bioscrape is FAST! \n",
    "* You can do tons of simulations very quickly to explore parameter space or large numbers of potential circuits\n",
    "* Uses Cython (special python code that compiles into C)\n",
    "* Multiple highly optimized simulators\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "_The benchmark test used for comparing the speed of these different simulators is a simple gene expression model consisting of just four stochastic reactions: transcription, translation, and degradation of mRNA and protein._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Bioscrape is Object Oriented and Customizable\n",
    "* Models, Simulators, Propensities, Delays, etc. are all objects so the code can be \"easily\" extended\n",
    "\n",
    "* **Available Simulators:** Deterministic ODE, Stochastic, Stochastic with Delay (See basic_examples_START_HERE notebook or Bioscrape Wiki), Stochastic Single Cell Lineage (Covered later in the course)\n",
    "\n",
    "* **Available Propensities:** Massaction, Hill Functions (Covered next week), General Algebraic Expressions (See Bioscrape Wiki)\n",
    "\n",
    "* **Other Core Objects:** Delays (See basic_examples_START_HERE notebook or Bioscrape Wiki), Rules (Covered next week), Model Interfaces\n",
    "\n",
    "_If you are interested in learning more or extending the software, let me know and I will organize a developer tutorial._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Other Similar Software\n",
    "\n",
    "For Simulating SBML (CRN) Models:\n",
    "* [iBioSim](https://github.com/MyersResearchGroup/iBioSim): Extensive suite for simulating metabolic and signalling networks. Included analysis tools. Also supports SBOL.\n",
    "* [COPASI](http://copasi.org/): \"Environment\" for creating and simulating SBML models. Written in C without a python API.\n",
    "* [SimBiology](https://www.mathworks.com/products/simbiology.html): MATLAB biological simulation toolbox. Lots of analysis functionality. Slower than bioscrape.\n",
    "* [libRoadRunner](https://academic.oup.com/bioinformatics/article/31/20/3315/195758): Very fast CRN simulation (C++) via SBML. Difficult to extend. Python API available.\n",
    "\n",
    "[Complete List Available Here](https://github.com/BuildACell/txtlsim-python/wiki/Modeling-and-Analysis-Tools-for-Synthetic-Biology). Thanks to Ayush Pandey for putting this together!"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# A Biochemical Signalling Model\n",
    "\n",
    "A signal cooperatively $S$ binds to a transcription factor $F^0$ changing its conformation to $F^1$. $F^1$ binds to a gene $G^0$ activating it to $G^1$. A polymerase $P$ can bind to $G^1$ and then transcribes a transcript $T$. The transcript can bind to a ribosome $R$ and be translated to a protein $X$. The signalling molecule, transcripts, proteins are assumed to degrade via dilution. Cellularly machinery ($F$, $P$, and $R$) DNA ($G$) are assumed to be a constant concentration.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "\\begin{align}\n",
    "2 S + F^0 &\\underset{k^u_1}{\\overset{k^b_1}\\rightleftharpoons} F^1\\\\\n",
    "F^1 + G^0 &\\underset{k^u_2}{\\overset{k^b_2}\\rightleftharpoons} G^1 \\\\\n",
    "G^1 + P &\\underset{k^u_3}{\\overset{k^b_3}\\rightleftharpoons} G^1 :P \\xrightarrow{k_{tx}} G^1 + P + T \\\\\n",
    "T + R &\\underset{k^u_4}{\\overset{k^b_4}\\rightleftharpoons} T :R \\xrightarrow{k_{tl}} T + R + X \\\\\n",
    "S &\\xrightarrow{\\delta} \\emptyset \\\\\n",
    "T &\\xrightarrow{\\delta} \\emptyset \\\\\n",
    "X &\\xrightarrow{\\delta} \\emptyset\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "#Using Bioscrape: Basic Imports\n",
    "\n",
    "#A Model is a CRN with some bells and whistles\n",
    "from bioscrape.types import Model\n",
    "\n",
    "#py_simulate_model is a helper function that takes care of may details for you\n",
    "from bioscrape.simulator import py_simulate_model\n",
    "\n",
    "#For arrays and plotting\n",
    "import numpy as np\n",
    "import pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "#A Model Requires:\n",
    "\n",
    "#Species: A list of all the species\n",
    "species = [\"S\", \"F0\", \"F1\", \"G0\", \"G1\", \"P\", \"G1:P\", \"T\", \"R\", \"T:R\", \"X\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "#A Model Requires:\n",
    "\n",
    "#Parameters: List of tuples [(\"paramter name\" (string), value (float))]\n",
    "parameters = [(\"k1b\", 100), (\"k1u\", 1), \n",
    "          (\"k2b\", 50), (\"k2u\", 10), \n",
    "          (\"k3b\", 50), (\"k3u\", 2), \n",
    "          (\"k4b\", 40), (\"k4u\", 10),\n",
    "          (\"ktx\", 2), (\"ktl\", 5), (\"delta\", .5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "#A Model Requires:\n",
    "\n",
    "#Reactions: A list of reactions [rxn1, rxn2...]. Each reaction is a tuple ([Input Species], [Output Species], \"propensity_type\", {propensity_parameters})\n",
    "\n",
    "#2S + F0 --> F1 @ k = k1b\n",
    "rxn1b = ([\"S\", \"S\", \"F0\"], [\"F1\"], \"massaction\", {\"k\":\"k1b\"})\n",
    "\n",
    "#F1 --> S + F0 @ k = k1u\n",
    "rxn1u = ([\"F1\"], [\"S\", \"S\", \"F0\"], \"massaction\", {\"k\":\"k1u\"})\n",
    "\n",
    "#All other binding/unbinding reactions\n",
    "rxn2b, rxn2u = ([\"F1\", \"G0\"], [\"G1\"], \"massaction\", {\"k\":\"k2b\"}), ([\"G1\"], [\"G0\", \"F1\"], \"massaction\", {\"k\":\"k2u\"})\n",
    "rxn3b, rxn3u = ([\"G1\", \"P\"], [\"G1:P\"], \"massaction\", {\"k\":\"k3b\"}), ([\"G1:P\"], [\"G1\", \"P\"], \"massaction\", {\"k\":\"k3u\"})\n",
    "rxn4b, rxn4u = ([\"T\", \"R\"], [\"T:R\"], \"massaction\", {\"k\":\"k4b\"}), ([\"T:R\"], [\"T\", \"R\"], \"massaction\", {\"k\":\"k4u\"})\n",
    "\n",
    "#Transcription\n",
    "rxntx = ([\"G1:P\"], [\"G1\", \"P\", \"T\"], \"massaction\", {\"k\":\"ktx\"})\n",
    "\n",
    "#Translation\n",
    "rxntl = ([\"T:R\"], [\"T\", \"R\", \"X\"], \"massaction\", {\"k\":\"ktl\"})\n",
    "\n",
    "#Degredation -- Notice reactions can share parameters!\n",
    "rxndt = ([\"T\"], [], \"massaction\", {\"k\":\"delta\"})\n",
    "rxndx = ([\"X\"], [], \"massaction\", {\"k\":\"delta\"})\n",
    "rxnds = ([\"S\"], [], \"massaction\", {\"k\":\"delta\"})\n",
    "\n",
    "reactions = [rxn1b, rxn1u, rxn2b, rxn2u, rxn3b, rxn3u, rxn4b, rxn4u, rxntx, rxntl, rxndt, rxndx, rxnds]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "#A Model Requires:\n",
    "\n",
    "#An initial condition for each species (uninitialized species default to 0)\n",
    "x0 = {\n",
    "    \"S\":200,\n",
    "    \"F0\":10,\n",
    "    \"G0\":1,\n",
    "    \"P\":25,\n",
    "    \"R\":100,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/ipykernel_launcher.py:4: UserWarning: The following species are uninitialized and their value has been defaulted to 0: F1, G1, G1:P, T, T:R, X, \n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "#Bioscrape is Object Oritened: Models, Propensities, Simulators, etc. are all Objects\n",
    "\n",
    "#Instantiate the Model [The only object must of us will care about]\n",
    "M = Model(species = species, reactions = reactions, parameters = parameters, initial_condition_dict = x0)\n",
    "\n",
    "\n",
    "#Simulate the Model\n",
    "timepoints = np.linspace(0, 500, 50000)\n",
    "\n",
    "Results = py_simulate_model(timepoints, Model = M) #py_simulate_model takes care of all other objects for you"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a247d0f10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/IPython/core/pylabtools.py:132: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bioscrape returns a Pandas Dataframe by default, so plotting is easy!\n",
    "\n",
    "plt.figure()\n",
    "plt.subplot(121)\n",
    "plt.title(\"Signal, Transcript, and Protein\")\n",
    "plt.plot(timepoints, Results[\"S\"]+Results[\"F1\"], label = \"S\")\n",
    "plt.plot(timepoints, Results[\"T\"]+Results[\"T:R\"], label = \"T\")\n",
    "plt.plot(timepoints, Results[\"X\"], label = \"X\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title(\"Machinery and Complexes\")\n",
    "plt.plot(timepoints, Results[\"F0\"], color = 'blue', label = \"F0\")\n",
    "plt.plot(timepoints, Results[\"F1\"], \":\", color = 'blue', label = \"F1\")\n",
    "plt.plot(timepoints, Results[\"P\"], color = \"cyan\", label = \"P\")\n",
    "plt.plot(timepoints, Results[\"G1:P\"], \":\", color = \"cyan\", label = \"G:P\")\n",
    "plt.plot(timepoints, Results[\"R\"], color = \"purple\", label = \"R\")\n",
    "plt.plot(timepoints, Results[\"T:R\"], \":\", color = \"purple\", label = \"T:R\")\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a263a98d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Because parameters are named, it is very easy and fast to change parameter values to understand how the effect a system.\n",
    "\n",
    "Tmax = []\n",
    "Xmax = []\n",
    "\n",
    "#Change the degredation rate of X, T, and S all at once\n",
    "deltas = np.logspace(-2, 2, 100)\n",
    "\n",
    "for delta in deltas:\n",
    "    M.set_parameter(\"delta\", delta) #It much more efficient to change model paramters than re-instantiate new models\n",
    "    Results = py_simulate_model(timepoints, Model = M)\n",
    "    Tmax.append(np.max(Results[\"T\"]))\n",
    "    Xmax.append(np.max(Results[\"X\"]))\n",
    "    \n",
    "plt.figure()\n",
    "plt.loglog(deltas, Tmax, label = \"max T\")\n",
    "plt.loglog(deltas, Xmax, label = \"max X\")\n",
    "plt.xlabel(\"Degredation Rate Delta\")\n",
    "plt.ylabel(\"Max Concentration\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a26370050>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Or we could get a dose response curve by changing the initial condition of S\n",
    "\n",
    "#COMMON ERROR ALERT!\n",
    "#Reset the paramter delta - parameter changes are permanent until you reset them\n",
    "M.set_parameter(\"delta\", .5)\n",
    "\n",
    "Tmax = []\n",
    "Xmax = []\n",
    "\n",
    "#Different initial values of S\n",
    "S0s = np.logspace(-2, 2, 100)\n",
    "\n",
    "for s0 in S0s:\n",
    "    x0[\"S\"] = s0 #Change my initial condition dictionary\n",
    "    M.set_species(x0)\n",
    "    Results = py_simulate_model(timepoints, Model = M)\n",
    "    Tmax.append(np.max(Results[\"T\"]))\n",
    "    Xmax.append(np.max(Results[\"X\"]))\n",
    "\n",
    "plt.figure()\n",
    "plt.loglog(S0s, Tmax, label = \"max T\")\n",
    "plt.loglog(S0s, Xmax, label = \"max X\")\n",
    "plt.xlabel(\"[S]\")\n",
    "plt.ylabel(\"Max Concentration\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a278f4190>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/anaconda3/envs/python3.7-bioscrape/lib/python3.7/site-packages/IPython/core/pylabtools.py:132: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Remember to reset modified variables!\n",
    "x0[\"S\"] = 200\n",
    "\n",
    "#Switching to Stochastic Simulation is easy!\n",
    "Results_stoch = py_simulate_model(timepoints, Model = M, stochastic = True) #py_simulate_model takes care of all other objects for you\n",
    "\n",
    "#Same plotting as before...\n",
    "plt.figure()\n",
    "plt.subplot(121)\n",
    "plt.title(\"Signal, Transcript, and Protein\")\n",
    "plt.plot(timepoints, Results_stoch[\"S\"]+Results[\"F1\"], label = \"S\")\n",
    "plt.plot(timepoints, Results_stoch[\"T\"]+Results[\"T:R\"], label = \"T\")\n",
    "plt.plot(timepoints, Results_stoch[\"X\"], label = \"X\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title(\"Machinery and Complexes\")\n",
    "plt.plot(timepoints, Results_stoch[\"F0\"], color = 'blue', label = \"F0\")\n",
    "plt.plot(timepoints, Results_stoch[\"F1\"], \":\", color = 'blue', label = \"F1\")\n",
    "plt.plot(timepoints, Results_stoch[\"P\"], color = \"cyan\", label = \"P\")\n",
    "plt.plot(timepoints, Results_stoch[\"G1:P\"], \":\", color = \"cyan\", label = \"G:P\")\n",
    "plt.plot(timepoints, Results_stoch[\"R\"], color = \"purple\", label = \"R\")\n",
    "plt.plot(timepoints, Results_stoch[\"T:R\"], \":\", color = \"purple\", label = \"T:R\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Collecting Statistics from a single stochastic trajectory\n",
    "\n",
    "The below example illustrates how important it is to be careful when averaging over a stochastic trajectory. Including the early part of a stochastic simulation in a steady-state average can result in bias due to transient behavior. Not sampling the steady-state \"typical set\" sufficiently can result in noisy statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a28ffe190>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Burn in and sampling noise in one simulation\n",
    "#This matters for steady-state statistics\n",
    "#time dependent statistics require many simulations (see below)\n",
    "\n",
    "Results_stoch = py_simulate_model(timepoints, Model = M, stochastic = True) #py_simulate_model takes care of all other objects for you\n",
    "\n",
    "plt.figure(figsize = (9, 5))\n",
    "plt.subplot(121)\n",
    "percentages = np.arange(1, 0, -.01) \n",
    "plt.plot(percentages,[np.mean(Results_stoch[\"X\"][int(t*len(timepoints)):]) for t in percentages], label = '\"Accurate\" Statistics')\n",
    "plt.plot(percentages[:15],[np.mean(Results_stoch[\"X\"][int(t*len(timepoints)):]) for t in percentages[:15]], label = \"Short Noisy Trajectory\")\n",
    "plt.plot(percentages[75:],[np.mean(Results_stoch[\"X\"][int(t*len(timepoints)):]) for t in percentages[75:]], label = \"Non-steady-state included\")\n",
    "plt.ylabel(\"Mean(X)\")\n",
    "plt.xlabel(\"Final Fraction of\\nSimulation Trajectory Used\")\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "plt.subplot(122)\n",
    "\n",
    "plt.plot(percentages,[np.std(Results_stoch[\"X\"][int(t*len(timepoints)):]) for t in percentages], label = '\"Accurate\" Statistics')\n",
    "plt.plot(percentages[:15],[np.std(Results_stoch[\"X\"][int(t*len(timepoints)):]) for t in percentages[:15]], label = \"Short Noisy Trajectory\")\n",
    "plt.plot(percentages[75:],[np.std(Results_stoch[\"X\"][int(t*len(timepoints)):]) for t in percentages[75:]], label = \"Non-steady-state included\")\n",
    "plt.ylabel(\"Standard Deviation(X)\")\n",
    "plt.xlabel(\"Final Fraction of\\nSimulation Trajectory Used\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Collecting Samples from Multiple Stochastic Trajectories\n",
    "The below example illustrates how multiple stochastic trajectories allow for time-dependent statistics, including transient and steady-state dynamics. There is a computational trade-off between the time spent on individual trajectories and the number of trajectories simulated. For time-dependent statistics, more trajectories may be more important than simulating them longer. For steady-state statistics, fewer longer simulations may be better. That said, these rules of thumb depend on the system of interest and may become very complex in large multi-modal systems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mean X')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#If we do lots of simulations, we can calculate both better steady state results AND time-dependent statistics\n",
    "\n",
    "N = 20 #Number of simulations\n",
    "\n",
    "timepoints = np.linspace(0, 200, 1000) #New Timepoints for faster simulations\n",
    "\n",
    "samples = np.zeros((N, len(timepoints)))\n",
    "for i in range(N):\n",
    "    Results_stoch = py_simulate_model(timepoints, Model = M, stochastic = True)\n",
    "    samples[i, :]= Results_stoch[\"X\"]\n",
    "\n",
    "    \n",
    "#Calculate Time Dependent Statistics\n",
    "meanX = np.mean(samples, 0)\n",
    "std100 = np.std(samples, 0) #100% of samples used to calculate standard deviation\n",
    "std50 = np.std(samples[:int(N/2), :], 0) #50% of samples\n",
    "std10 = np.std(samples[:int(N/10), :], 0) #10% of samples\n",
    "\n",
    "#Calculate Steady State Statistics\n",
    "ss_start = 750 #Time index to start steady state calculation\n",
    "meanXss = np.mean(meanX[ss_start:]) #Steady State Mean\n",
    "std100ss = np.std(samples[:, ss_start:]) #100% of samples used to calculate steady state standard deviation\n",
    "std50ss = np.std(samples[:int(N/2), ss_start:]) #50% of samples\n",
    "std10ss = np.std(samples[:int(N/10), ss_start:]) #10% of samples\n",
    "\n",
    "\n",
    "#Plotting below\n",
    "plt.figure(figsize = (9, 5))\n",
    "plt.subplot(121)\n",
    "plt.title(\"Time Dependent Statistics\")\n",
    "\n",
    "plt.plot(timepoints, meanX, color = 'blue', label = \"mean N=\"+str(N))\n",
    "plt.plot(timepoints, meanX+std100,\"-.\", color = 'blue', alpha = .75, label = \"Standard Devation N=\"+str(N))\n",
    "plt.plot(timepoints, meanX-std100,\"-.\", color = 'blue', alpha = .75)\n",
    "plt.plot(timepoints, meanX+std50,\":\", color = 'blue', alpha = .5, label = \"Standard Deviation N=\"+str(int(N/2)))\n",
    "plt.plot(timepoints, meanX-std50,\":\", color = 'blue', alpha = .5)\n",
    "plt.plot(timepoints, meanX+std10, color = 'blue', alpha = .25, label = \"Standard Deviation N=\"+str(int(N/10)))\n",
    "plt.plot(timepoints, meanX-std10, color = 'blue', alpha = .25)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"mean X\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title(\"Zoom into Steady State\")\n",
    "\n",
    "plt.plot(timepoints[ss_start:], meanX[ss_start:], color = 'blue')\n",
    "plt.plot(timepoints[ss_start:], meanX[ss_start:]+std100[ss_start:],\"-.\", color = 'blue', alpha = .75)\n",
    "plt.plot(timepoints[ss_start:], meanX[ss_start:]-std100[ss_start:],\"-.\", color = 'blue', alpha = .75)\n",
    "plt.plot(timepoints[ss_start:], meanX[ss_start:]+std50[ss_start:],\":\", color = 'blue', alpha = .5)\n",
    "plt.plot(timepoints[ss_start:], meanX[ss_start:]-std50[ss_start:],\":\", color = 'blue', alpha = .5)\n",
    "plt.plot(timepoints[ss_start:], meanX[ss_start:]+std10[ss_start:], color = 'blue', alpha = .25)\n",
    "plt.plot(timepoints[ss_start:], meanX[ss_start:]-std10[ss_start:], color = 'blue', alpha = .25)\n",
    "\n",
    "plt.hlines(meanXss, timepoints[ss_start], timepoints[-1], lw = 2, color = 'red', label = \"Time Averaged Mean N=\"+str(N))\n",
    "plt.hlines(meanXss+std100ss, timepoints[ss_start], timepoints[-1], ls = \"-.\", alpha = .75, color = 'red', label = \"Time Averaged Std N=\"+str(N))\n",
    "plt.hlines(meanXss-std100ss, timepoints[ss_start], timepoints[-1], ls = \"-.\", alpha = .75, color = 'red')\n",
    "plt.hlines(meanXss+std50ss, timepoints[ss_start], timepoints[-1], ls = \":\", alpha = .5, color = 'red', label = \"Time Averaged Std N=\"+str(int(N/2)))\n",
    "plt.hlines(meanXss-std50ss, timepoints[ss_start], timepoints[-1], ls = \":\", alpha = .5, color = 'red')\n",
    "plt.hlines(meanXss+std10ss, timepoints[ss_start], timepoints[-1], alpha = .25, color = 'red', label = \"Time Averaged Std N=\"+str(int(N/10)))\n",
    "plt.hlines(meanXss-std10ss, timepoints[ss_start], timepoints[-1], alpha = .25, color = 'red')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"mean X\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## In this example, we compare stochastic and deterministic dynamics. For what parameter values are the two qualitatively similar and different?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a26aa4bd0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What species values will make these model qualitatively different?\n",
    "\n",
    "#Simulate deterministic versus stochastic\n",
    "#Original Values: x0 = {\"S\":200, \"F0\":10,\"G0\":1,\"P\":25,\"R\":100}\n",
    "x0 = {\n",
    "    \"S\":200,\n",
    "    \"F0\":10,\n",
    "    \"G0\":1,\n",
    "    \"P\":25,\n",
    "    \"R\":200,\n",
    "}\n",
    "M.set_species(x0)\n",
    "timepoints = np.linspace(0, 500, 1000)\n",
    "Results_det = py_simulate_model(timepoints, Model = M, stochastic = False) \n",
    "Results_stoch = py_simulate_model(timepoints, Model = M, stochastic = True)\n",
    "\n",
    "\n",
    "#Same plotting as before...\n",
    "plt.figure()\n",
    "plt.title(\"Signal, Transcript, and Protein\")\n",
    "plt.plot(timepoints, Results_stoch[\"S\"]+Results_stoch[\"F1\"], \":\", label = \"S stoch\", color = 'blue')\n",
    "plt.plot(timepoints, Results_stoch[\"T\"]+Results_stoch[\"T:R\"], \":\", label = \"T stoch\", color = 'cyan')\n",
    "plt.plot(timepoints, Results_stoch[\"X\"], \":\", label = \"X stoch\", color = 'purple')\n",
    "\n",
    "plt.plot(timepoints, Results_det[\"S\"]+Results_det[\"F1\"], label = \"S det\", color = 'blue')\n",
    "plt.plot(timepoints, Results_det[\"T\"]+Results_det[\"T:R\"], label = \"T det\", color = 'cyan')\n",
    "plt.plot(timepoints, Results_det[\"X\"], label = \"X det\", color = 'purple')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving and Loading Models\n",
    "\n",
    "Bioscrape has its own internal XML language which can be used to save and load CRN models. This is helpful for keeping track of and sharing models. Bioscrape can also load SBML (system biology markup language) models. However, bioscrape cannot save its models as SBML because Bioscrape models may contain features which are not included in the SBML format.\n",
    "\n",
    "Examples for loading SBML models can be found at the end of the basic_examples_START_HERE notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x219fdf6cf08>]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Write the model M to a file\n",
    "#Note that bioscrape saves the current state of the model and will not auto-save any changes to it\n",
    "file_name = 'SignalTxTl.xml'\n",
    "M.write_bioscrape_xml(file_name) \n",
    "\n",
    "M_loaded = Model(file_name)\n",
    "\n",
    "#M_SBML = import_sbml('sbml_file.xml') #This code won't work without first creating an SBML File\n",
    "\n",
    "#And the simulation will look the same as before\n",
    "Results = py_simulate_model(timepoints, Model = M_loaded, stochastic = False)\n",
    "\n",
    "plt.plot(timepoints, Results[\"S\"]+Results[\"F1\"], label = \"S det\", color = 'blue')\n",
    "plt.plot(timepoints, Results[\"T\"]+Results[\"T:R\"], label = \"T det\", color = 'cyan')\n",
    "plt.plot(timepoints, Results[\"X\"], label = \"X det\", color = 'purple')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With any numerical software, errors are bound to occur. Below we explore the most common error: numerical errors, in the stochastic and deterministic regime.\n",
    "\n",
    "1) Deterministic Blow-up usually results in an error from scipy.odeint: \"...odeint failed with mxstep=500...\" and sometimes a user warning about \"excessive computation\". This can occur due to numerical rounding errors, stiff ODEs, or numerical blow-up.\n",
    "* To identify numerical blow-up, decrease the integration time and increase the number of time steps (decrease dt).\n",
    "* To identify stiff equations, check to see if your rate constants span many orders of magnitude - if this is the case it may be possible to use a model reduction to improve integration (more details coming next lecture).\n",
    "* Rounding errors might make ODEint complain, but typically can be ignored.\n",
    "\n",
    "2) Stochastic blowup usually results in excessive time needed to run stochastic simulations or simulations that never finish. Sometimes, after breaking the stochastic simulator, the entire kernel needs to be restarted. These errors typically occur due species counts that are too large for the simulator to handle. If this is the case, a deterministic simulation might be more appropriate for your system. If species counts' don't appear to be the error, another common source is user defined propensities or rules resulting in improper values. These can be harder to catch and require care from the modeller\n",
    "* To identify if the problem is due to large species counts, try running the simulation for much less time - if some species value is going very large, you can often catch it this way.\n",
    "* **Safe Mode:** You can also try the keyword safe=True in py_simulate_model. This simulates the model in \"safemode\" which will try to catch blow up errors and other numerical errors in when simulating stochastic models. Safe-mode is not supported for deterministic simulations. Additionally, safemode has considerable overhead and can slow down simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x219fc47e6c8>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Common Errors:\n",
    "\n",
    "#Deterministic:\n",
    "#Original values: 'delta':0.5,\n",
    "\n",
    "#By making delta very small and the integration time very large, the simulator gets values that are too large.\n",
    "#In the deterministic case, ODEint will raise a warning.\n",
    "\n",
    "#In the stochastic case, the simulation might just not seem to work. \n",
    "#Sometimes, after breaking the stochastic simulator, you have to re-run the entire notebook.\n",
    "\n",
    "M.set_parameter('delta', .00000001)\n",
    "timepoints = np.linspace(0, 500, 1000) #by shortenning the integration time, this error will go away.\n",
    "Results = py_simulate_model(timepoints, Model = M, stochastic = False, safe = False)\n",
    "\n",
    "plt.plot(timepoints, Results[\"S\"]+Results[\"F1\"], label = \"S det\", color = 'blue')\n",
    "plt.plot(timepoints, Results[\"T\"]+Results[\"T:R\"], label = \"T det\", color = 'cyan')\n",
    "plt.plot(timepoints, Results[\"X\"], label = \"X det\", color = 'purple')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Things to Try:\n",
    "Model a circuit/part/subsystem relevant to your research. To begin, write down a CRN that seems to describe all the chemical steps in your system (no simplifications such as hill functions - if you model all the chemical species' interactions step by step you should be able to model any system using mass-action kinetics. It is okay if this CRN is large and if you don't know all the parameter values - make a best guess or ask the TAs for help getting a ballpark estimate. Then, try varying some parameters (perhaps ones you could imagine manipulating experimentally). For example, you could titrate an inducer or set the value of a gene to 0 (knockout) or double it (over-expression). Does your circuit behave qualitatively differently when modelled stochastically?\n",
    "\n",
    "If you don't have a circuit/system in mind, try modelling these two below:\n",
    "* A bistable toggle switch: https://www.ncbi.nlm.nih.gov/pubmed/10659857\n",
    "* The repressilator: https://www.nature.com/articles/35002125\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "### References:\n",
    "**Simulating Chemical Reaction Networks:**\n",
    "* [Daniel T. Gillespie. Stochastic Simulation of Chemical Kinetics](http://cctbio.ece.umn.edu/wiki/images/7/78/Gillespie-Daniel-T_Stochastic_Simulation_of_Chemical_Kinetics.pdf). How stochastic CRN simulations work.\n",
    "* [Swaminathan et al. Fast and flexible simulation and parameter estimation for synthetic biology using bioscrape](https://www.biorxiv.org/content/10.1101/121152v2). The paper introducing Bioscrape.\n",
    "\n",
    "**Chemical Reaction Networks in Biology**:\n",
    "* [Domitilla Del Vecchio and Richard M. Murray. Biomolecular Feedback Systems](http://www.cds.caltech.edu/~murray/BFSwiki/index.php/Main_Page). An excellent introduction to biochemical models, simulation, and theory.\n",
    "\n",
    "**Mathematical Theory of Chemical Reaction Networks**:\n",
    "* [Jeremy Gunawardena. Chemical reaction network theory for in-silico biologists](http://vcp.med.harvard.edu/papers/crnt.pdf). An introduction into some of the deeper theory of chemical reaction networks with many great references.\n",
    "* [Soloveichik et al. Computation with finite stochastic chemical reaction networks](https://link.springer.com/article/10.1007/s11047-008-9067-y). Discusses the computational complexity of chemical reactions networks.\n",
    "\n",
    "**Physics of Chemical Reaction Networks**:\n",
    "* [Tim Schmiedl and Udo Seifert. Stochastic thermodynamics of chemical reaction networks.](https://aip.scitation.org/doi/full/10.1063/1.2428297). Detailed thermodynamic treatment of stochastic chemical reaction networks.\n",
    "* [Riccardo Rao and Massimiliano Esposito. Nonequilibrium Thermodynamics of Chemical Reaction Networks:\n",
    "Wisdom from Stochastic Thermodynamics.](https://arxiv.org/pdf/1602.07257.pdf). A detailed thermodynamic treatment of deterministic chemical reaction networks including many non-equilibrium examples."
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "metadata": {},
   "outputs": [],
   "source": []
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