{
 "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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5c80emGvr9V8HkBWUbpDAyJNAzgmxf54o+aK0+SuArACymixWAWS1tOAVeec/v8Zc7t+vLZUr5qSfBbL4ItM6vifPgyx5Gsomp7sK43tlt2tgZ16OSiCfXXAt6EqlGEDWyFOwYcRBAjknxNQ5o4TM0uavALICyAogK99voMg7D8j64LUlcsUc9Dk14HURqYjIKyKyc3x+pp6hyT9fx8fHS4Fjht0fq4S5TiSArKBwgwRGngRyTIilzC+dJhJAVj4F2wREZvVlsyxrVVbZrD7uQfJf5J3/3Bqj3D9eXTxXLEK/0/NDV9APIGvkKdgw4iCBXpkQA8gKIKsJQAaQVepyISDr3VcXyxV7ZU4pHZgFkBUUbpDAyJNAr0yIAWQFkBVAVr7fQJF3/rNrjHJvv7porliEfumAphsJBpA18hRsGHGQQK9MiAFk5VOwTUBkkMtOw04ry1qVVTarj3uQ/Bd55wFZb766SK5YhH43YqLSeQogKyjcIIGRJ4FemRADyAogqwn0BZBV6nLhGmuMcq++snCu2CtzSgBZI08fhhEHCZQugV6ZEAPIGoEga5AWnSZQNoJoFXnnV19jTvfSK6NzxSL0Swc03UgwWLJK11+BYJBA10ugVybEALICyBqpoGmg4y7yzq+++pzuhZdH54pF6HcjJiqdpwCyul4fBgaDBEqXQK9MiAFkBZA1ULAxUusXeedXW31O9/zLo3PFIvRLBzTdSDCArNL1VyAYJND1EuiVCTGArACyRipoGui4i7zzq60+yj3z8sK5YhH63YiJSucpgKyu14eBwSCB0iXQKxNiAFkBZA0UbIzU+kXe+VVXH+Uef2mRXLEI/dIBTTcSDCCrdP0VCAYJdL0EemVCDCArgKyRCpoGOu4i7/wqq49yj7y0aK5YhH43YqLSeQogq+v1YWAwSKB0CfTKhBhAVgBZAwUbI7V+kXcekDVtxmK5YhH6pQOabiQYQFbp+isQDBLoegn0yoQYQFYAWSMVNA103EXe+ZVXH+UenLF4rliEfjdiotJ5CiCr6/VhYDBIoHQJ9MqEGEBWj4Isu6lo8noE7W01UCCVVb/IO7/S6nO5+2YsmSsWoV86oOlGggFkla6/AsEgga6XQK9MiAFkBZCVBSxCWeP3UeSdB2RNeXGpXLEI/W7ERKXzFEBW1+vDwGCQQOkS6JUJMYCshhLtKUCRtF7Z+2DJanl0TtZvoMg7v+Lqc7vbX1g2VyxCv3RA040EA8gqXX8FgkECXS+BXpkQA8gKICsLWISyxu+jyDu/wupzu1tfWD5XLEK/GzFR6TwFkNX1+jAwGCRQugR6ZUIMIKuhRHsKUFjLVfI6WLKGzJIFyLr5hRVyxV6ZUwLIKl3dBIJBAiNPAr0yIQaQFUBWT4HLDgLIIu/88qvN426YvlKuWIR+6YCmGwkGS9bIU7BhxEECvTIhBpAVQFYAWfl+A0XeeUDWddNXzhWL0O9GTFQ6TwFkBYUbJDDyJNArE2IAWfkU7CwHRJJLhPa+g9aeWU5OA5BFkXd+udU+4q58fvVcsQj90gFNNxIMIGvkKdgw4iCBXpkQA8gKIKuXgVGZYyvyzi+72kfc5c9/NlcsQr8bMVHpPAWQFRRukMDIk0CvTIgBZPUoyBqAhaZMINLLtIq884CsS577XK5YhH7pgKYbCQaQNfIUbBhxkECvTIgBZAWQ1cvAqMyxFXnnl1ltXnfhc2vmikXodyMmKp2nALKCwg0SGHkS6JUJMYCsALLKBCK9TKvIOw/IOv/ZL+SKReiXDmi6kWAAWSNPwYYRBwn0yoQYQFYAWb0MjMocW5F3funV5nXnPLt2rliEfjdiotJ5CiArKNwggZEngV6ZEAPICiCrTCDSy7SKvPNLrTaf+8sz6+SKReiXDmi6kWAAWSNPwYYRBwn0yoQYQFYAWb0MjMocW5F3fslV53N/fnrdXLEI/W7ERKXzFEBWULhBAiNPAr0yIQaQFUBWmUCkl2kVeeeXXHV+d+rT/5MrFqFfOqDpRoIBZI08BRtGHCTQKxNiAFkBZPUyMCpzbEXe+SVWnd+d9NR6uWIR+t2IiUrnKYCsoHCDBEaeBHplQgwgK4CsMoFIL9Mq8s4Dso5/6mu5YhH6pQOabiQYQNbIU7BhxEECvTIhztIgyx4Vw3WRDTgtjSLtk20svSRPAylL1tV709/Gc27lbNxI66SkeetZGlltssosjczrjLFsPGprpzGTRspYo/qGdvS7aFWP/GTd+N6Okesi7/ziq37UHfvkBrliEfrdiIlK5ymArKBwgwRGngR6ZUIMIGtL16/EWyjbVko4NT+pzC3NgZQl6+q9oZcEAf3j0LomtXWz6tmyrDZZZZZG5nXGWBRgkWbSMGNsqmdoDyfIWmzVj7qJT2ycK/bKnBJA1sjTh2HEQQKlS6BXJsQAsgLIagInBrRkAamsslb0Zso3QMjSi657xJK12KoLuKOfGJMr9sqcEkBW6eomEAwSGHkS6JUJMYCsALJmAj8x0LLAJ1knqyxZt+X9CABZi666gDvy8XG5Yq/MKQFkjTx9GEYcJFC6BHplQgwgK4CsViAoC0hllbWiN1P+CAFZhz3+DZcn9sqcoiBrcRG5RUSeFJHHReRnccEnRORGEXk2ThfUBq3S4JNVuv4KBIMEul4CwzwhljZ/BZAVQNZM4CdYsiJneAskuS7yzi+yysfcIY99M1fMQX+ciDwtIs+JyH4pmGSJGNc8JCKPiMgmKXWGLGthEVkz7u2jIvKMiKwiIkcZ5hnEke04CiCr6/VhYDBIoHQJ5JgQ200dgykvbf4KICuArACy0rfxKANkLbzKx9zvH/1OrthmTplDRJ4XkWVEZC4ReTjGLHYeOUVEdo8zwDMv2sLhvr5CRDaOUSITGIEU1JgZAsgqXX8Fgl0ggX/8o+6uuabuPvyw3gXcOFer1V293h28IJA2E2LmnNGBwsLzVwBZAWQFkNVJkPVx95tH/jdXbDOnrCMi15u5Y38RIdpwsojsG2dQ/25bOJzXS4nISyKygIi8l2Dk3cS93v4wFsgDSyyxRFcoocBEkECZEnj++bo788y6mz59+IEN4KpaDSBLJ59EOqj5ax6Zt+U+QalbFBg/mlCerpyDXHpTLm1AUOK19LejV/m4O+DhTXPF2PL0gGILEQFnaNhcRE7TGxHZXkSOM/dcYhh6VEReERGwy1qJ8mG5nV9EHhSRzeLe84KsfmaDJatM1R5odYsEnnrKg6x77+0ekIU1q1tCkQm3f9Io72LQ89csbckKgC8A5CH8DRR55wFZ+z783VyxDf0tUkDWsYmp5Oci8os4D0vWEyIye6LOkN6Ois1vMKaB5cGwXNgtmizwMWwSePRRD7Kuumr4gY1asgLI0mkqSkuZvwLI6k2rS7Cmlf9c24CgppdTbxZaZUG3z7TNc8U29PMsF/IRHx/FaJguIp/Rm6FOZxORs0Tkj4mOj044vuMInxmCJWvYcEDouIMSmDrVg6yzzqq7SmV4gRbgiuXCALL6p6LS5q8AsspXxgHg9KZM24Cg/pfTXgCyfjFty1yxDf05RQTQtLRxfF/V9iUi14rIjnHeyiLymogwVwxL+B8RwXmVzxynxZHPHT8pIjfHWziQsqVDZgggq4OaPpAeNglMmeJBFn5Zr7/eHSALoNUtoc2EmDlnlFBY2vwVQFZvAoIA9Mp/rkXe+c+svKDbc+r3csUc9MEo7ITAV4a/jueRg0TkO/E1XxTeFX95CK4ZU8JcM/wkAsjqFrUX+ChTAnfcUXfnneeB1iOPDC+4UUtWAFnlz3cBZJWvjAPA6U2Z5gBBM72gn175E+4nD26TKxahP1OHvZgRQFaZqj3Q6hYJTJ5cd1deWXeXX153N988vCALcKWxW+TTKxNiAFm9CQgC0Cv/uRZ55wFZuz+4ba5YhH4vYqqZxhRAVreovcBHmRK4/vq6u+66urvrrro7//zhB1lqzeqWvbJ6ZUIMIKt8ZRwATm/KtMg7/6mVP+F2e2C7XLEI/ZkASS9mBJBVpmoPtLpFAnxViAXrmWf8kiGbkw5XwIoVQFZnZs8AsnoTEASgV/5zLQKCPrXyJ92u9++QKxah35lZocuoBpA1XKo39NtJCVx6ad3dfnvdvfuuB1nPPTc8IEu3b7BpJ8edl3avTIhdD7Lic/T6dyW3+yIlyjYetbXrj3Nu5fRYlP62Oc7ks3X7aUHX0ON6zNzb9MemMsvDqK3dmHm2a0TTJmrfqmy+HdwYG2072yZ5Pe/2boxG255rU7dpXEl+tT1pRr92zE315t6m8QwYv6GX7NfylEnD8N7UZp7tmvqyzy55bfueqa/Es7Vjs9dF3nlA1s73fz9XLEK/y+BQZ9gJICuvWgr1ZiUJXHBB3fGFIeAGB/h77ukOkNUt2zj0yoQYQJY5VicnaEPxWkVtFbFV5lw3gQILWri24MGWJQGSLbNtktcG0DSBtACymsCYfXZcNz2/DMBV5J3/5EqfdDvc+4NcsQj9zqCaLqMaQNasBB0Cr3klcPbZdffggx5Y3Xijd4LP27bMemrBgqYuG5ZJvyitXpkQA8gKIMsCQ2t5agIjCUBngUlTvWDJakIon1jpU267KTvnir0ypzQJoIybALKKqqnQrlslAJhhfyzduuHhh/39f/879NYs9cVCVlwHS1YZs1aDRgBZAWQFkNVYWrbgMXldBAQBsraZskuuWIR+403u4asAsroVKgS+ikrgww89qOL8QsJrr/l70qEOFlhZwDXUfCT765UJMYCsALICyOocyFpwpU+5792za67YK3NK6XAvgKyk+gn3s7oE3n/fg6rnn/egqq/P30+bFkCWPttemRADyAogK4CszoKsLe/ezeWJvTKnBJClWiKkQQItJPDOOx5UvfRSA1SxMSm+WUMdrB+W9c8aaj6S/fXKhBhAVgBZAWR1DmR9fMVPu83u2j1X7JU5JYCspLaYBe9RtlmhXXlW28GWDWffg+Vd23NWYfLMQr4u5CvDoRrfCy/UHcuWgCztk9TeK7+klCWjLS/7ulcmxACyAsgKIKuzIOt/7/yxyxN7ZU4JIKtsbTMM9PDNUcWb7J784XSObgUCknx28z0WLEAWFi0N7JNFHvtmdTqoTxhfNyblmbyHF33m6r+laSf57JUJMYCsALICyOocyPrYip9237ljj1yxV+aUALI6qXmGgLYq1HYgq1V5J1mkT0DAcIK8MsaHLxaACt8sDez4Th47wHc66HLltdfWBgSyLF88g07+BnplQux6kGU3Hw3XLuzkXv5O7nllWuSdB2R96/af5IpF6JcOaLqRYHB8t6qt89coziwF2q68kxwqyAJozcrhySc9oMKiZANnGHKWYaeDWtLOPrvm+vpqTd2lgVh95rZi1m/E1it63SsTYgBZw6e08yr3UK87nlGRd36BFT/jNrntp7liEfrdiIlK5ymArKJqqlg7VaikaQHlqjGtvJN5vQKy2B8Lq1USLHKW4WWXpcu9TLkqyPvLX2rub39rBllpz1Z/E5aHtDxbPtjrXpkQA8jqDgUegFT3P4ci7zwga9xte+aKReiXDmi6kWAAWYNVVwNrr8oTZZsWVAm3Kk9rU1YefQJMiPA5q4YHHqi7v/51Zv4ffdSDr//8Z+ayMsdK/4C8M86oucceaw+ykHtS3vo7KZMvS6tXJsQAsrpfuQcA1h3PqMg7/9EVPuM2umWvXLEI/W7ERKXzFECWVT2dv1bl2QpEka8xqXg7zZ32m7ak1em+y6TPl4ScXZgMb7zhwc/LL89clqw7mHsOpr7kEniouVtvnRlkJS1syD35rPV3kswfDF+2ba9MiAFkdYcCD0Cq+59DkXcekLXh5L1zxSL0Swc03UgwgCyrejp/rcoTxZoWVOFqmlanU3n0aWOn+uk0XUDOpZfOLN9Kpe7OOqvupk6duaxMnq69tu6uu67uJk+uuQsvbAZZPP88IAt+Ovkb6JUJMYCs7lfuAYB1x4qqpBkAACAASURBVDMq8s7Pv8JC7ms3/zxXLEK/GzFR6TwFkFWmem1PayAgCyU7lEEtWPSbBAJDycdg+8L36qqr0mU3aVLdXX99etlg+9X2WLHuuIOzE2vRkuG//tXoL4CscqewALK6Q4EHINX9z6EICAJkrX/TL3LFIvTLnQ26lFoAWaoahybNAllaBif2emg4c/2+WAoESGfFgCWpFZC69966O+ecmZfnyhonMjv77Lpjj6w33qg5nN9ffLEhx6RsuW9lsdKysnizdHplQgwgq/uVewBg3fGMirzzgKyv3vTLXLEI/S6FReWyFUCWVT2dv1bFmaZYtQwu7HXnufL9Yb2iX4K9Hor+y+yDI3QmT24AG0t7+nTvl2U3KrXlg73+9789fQ6nZvsGtnG4//5mXqxss55zVtlg+eyVCbGjIGv2jI0+52y9AeSYebZz/XHubdwYE8d+dEen0eZH16ad1onSBXZyY200NJrqLbiLG2ujaTNmvh2cjZbeuE/t5jQ20aMfQyN5bemNW2h31x8NPaXbny7yEzcujk28Wr4X3KWfH2039uM7u/74yV3dWI2JdpZ/y1+S9/720LG0P75zU99N9bRPTU072xfXM/WXIUetq2MlTcrG0p+JXyP7prLE76TIOz/f8gu5dW/cJ1csQr9cNNOl1ALIGqy6Glh7wJVGBTRKIalU04CY1i07pW+r/Lmm/1kx6HJdGu8ffNAAQWnlg8176y1Pf8YMvxHpNdfU3DXXNMvRyjn5zJP9d+o30CsTYgBZDdCWVMyqvEmtkk6CgCblnlDMlkby2tLsB1go/ACyeghkjXZfvn7fXLFX5pTSoVoAWUm11tl7VZqa2t6SCjetjq1f5rWCLKVJ34CBWTGw6SjLgq3ChRd6n6lW5YPJnzHDg6y33/Ygi+0cWD60srQANvnMk3136jfQKxNiAFkBZDWBSwMSLQhMAsQmC5WxSGEJsiCxqZ5asDQ17WxfSRA7U98trFpN/SYsdJZ+k7UKfjtqyRrtvnTdfrlir8wpAWQltdAsdq9KU1PLfjKvnQK2bQd7Td8WCCRB12DpD2X7dl8Q3nqr32KhEzw98YQHWR9+6EGWgq4332yAPmRNJLR7xu3Ki46hVybEALICyAogq7FU2wTADOBkGbXIOz/v8qPdF6/dP1csQr90QNONBIMlq6iaKtZOgVSa8tQypZxWR8vKTunbgizocw8Ps1JgmwY2AmXj0Vbh8cd9Hfynyg74X7ERqspTfbToUwNlRAJplow79RvolQkxgKwAsgLI6iDIWm60+8I1++eKvTKnlI7TAshS1Tc0qSrVNOWpZZaTtDxbXtY1/RBtsMtaNr+brxXUPP1081gsz1iVAGJYmcoOt93m9+iy8sRHDOuZBsoU0HKdBbJok6eO0s6b9sqEGEBWAFkBZHUWZK15zQEuT+yVOSWArLxapEvrqcLsNpCVBqjglTgrhX/8wwMoviJsFRgrflL4S5UdcHJn+wgrOzZHveiiRl88+wCyypnKAsgKICuArM6BrI8st7D77KTf5IoBZLWY04Ilq2w1m02vCMgaCqCjIIvltv/+18f//Kfm/v3vWv+95melCh6ypeBL21lw8tBI1tGv+9odnQMYYj+tssPFF9fdnXd6EKXPje0csJzxZSOhCMhSWmn8FpFjr0yIAWQFkBVAVmdB1hpX/cblib0yp7SASsWzA8hKU1vl5gE8VBEmQZYqXNI0RdoqvyiHyoe257Bk+CMCqqZMqbu77/bxrrtq7o47ao5U85Jpsuy++xpj1T5apSzpPftsa6CT5LUVHZv/2mse0Pztb63pUl99p/L2wbOxz+fvf2+AJughv0rFbz56//3e6V1pv/12vWlTUvKpX6vVmmjqOLTc3tu+NV/Tvj6eXfZ4ta6mvTIhBpAVQFYAWZ0FWatf+VuXJ/bKnFIcTbVoGUCWqp3OpKowVUmSqvLlulr1CjlL4WrbMjikb+2f9MMPsVp5Ht56y4MprECAFOJrr9Xc66/X+u81n5QdzYma99xzHpxh6coT2BX94Ydb17W85qFHHf2aDxCUFdS6ZI+8yarvQZGnyfPAr+u99xp9UP7Pf3qQ9dRTtX4ZQxMQxM7vjz3WXL/VM/e/i+bnlPUbAGDRx0BCr0yIHQVZs3XHTt1hx/TwHMr4DRR55+dZdmG32hW/yxWL0G8BS3orO4CsgaimgdcFKKiC5toqS69Ms0EWPVKPtmUEC1ygC8jS7QamT/eWLNsXdYhpQWlp/Xff9SDr/ffT6ydp3HOPP34mma/3Sl/v86QAPZbm/vnPbB4AktRjebFdgA+eIZEAiARk6a7xWv63v9UimmlLleecg2wbh0V7eumWLO3Lyp1rlbPllzyeofJmy7Kue2VCDCArAJAyAMhIoFHknQdkrXL573PFIvR7C021GE0AWVmqaPBlqoBRglwnFSdLTFkKFw5aKdiBckf/GmlLvyho/K7g45FH6k3WFu27lQJXWqQErEIsJ7I81i5geaEuy5NpIUk7rU5anu5T1W75DEsXIMueK5hGjzx48c/I84pvFSBLAZqWv/CCB1lpVrTLL6+5m29ugCyeKTK3vwftX/uyZa1+A/oM9RkojXZpr0yIAWQFkDUSAFIZYyzyzgOyVr7s97liEfotYElvZQeQ1U4dDa4c5dhQms1KlaVCBVmkrRQl+VbhFuUIOhqhgZM7IOtf/6q5Dz+sOixLyW0NqA//pMmgtLSMegCnV1+duW6yLZYm9e9KG1uSdrJ9q3uWHwFPaTRtG0AY9QBl7YJ9htTFYgfIIiqfjF3330oCPOrceGPNXXVVA2SRlwayyIeW9qm8kZ82JsAqz3CgoVcmxACyAsgqA4CMBBpF3vm5l13ErXjJgbliEfq9haZajCaArIGqp4HVV2WJ4gRUWUWJTw6HCFM2HCBLFTQg6803qxHoSbPCwB9KPhnI06hlHGfzwgsz19VyTVlqU5CV5sOl/SXpa/tWKdsynHNO+/5pz6ahycOb0+j6Z9cAm1iwsNYBsgCq+oxx+k/rmzHwgcD55zdAFv3w7PkN2KC0aGPlzr397Wgb/dJT7/OmvTIhBpAVQNZIAEhljLHIOw/IWuGSA3PFIvRbwJKuyD5dRN4UkccMN38QkVdFZFocNzFlLS8DyMqrlorVQzFqTIIs7r2ixXG5tSWLnqGBoh1MoL1G6KCgsbrgk/X88x5kpTlQo+zTFLzSsnw99FDd4VTeLvAVoIKsNOdzpal9tKOn5dDkbMI84bLLmjcJbdVGx0/KcwJcYYkjRYbIhjI2HIVmMjCGadO8Uzz1NECL34AN+lshj7oqB+7TfgNYsQB6Aw1dMCGWMocFkBVAVhkAZCTQKPLOz73MIm75iw/KFXPQHyciT4vIcyKyXwtQsqWIPCEij4vIuS3qDEn2eiKyZgrI+uVAew8ga6DqaWD1UZSqOJPLQ9xrXqdBlgUrqrjVCsLWDQ89VHVTp6YrawURyZErHUub5Td8u9oFfKEUZNmv9Ghn6el9O3pazo7raUBHy216ww11x35ZWQFeFOyQfvCBB1mAUUAWO8zr87366rqDZjJA45lnPMiyHwXw7JMgS38v0LDX3CdBFveALNKBhhwT4kCnkoHWL2UOCyArgKyRAJDKGGORdz4CWRcd5JbPEdvQn0NEnheRZURkLhF5WERWSUway4vIQyKyYJz/mUT5kN8uFUDWQFXL0NdXRYmiTSpVvddlQ+q0CpQVUaZKj/ZKX1OsWIAFAN7tt1fd0083W1VsW8aRDErH0ubrvjw7qT/zTANkJR3lLT361H6S/afd33RT3QF28gQ2DbU7sae1QeY6dtJ33631O/YDsnCC12cMLWgmA/y/+qoHWW+80SgHYPEb0EA9aOl4bd/U4d7+BtSnTusrnTxpmwlxqCazQc9hAWQFkFUGABkJNIq884Cs5S48OFdsQ38dEbneTCz7iwjRhqNEZBebMdzXaRPUiyLyiIhgilc0mMbnD2OBPLDEEkvkmZNDnYISsEpTQRWkUIx6Pxwgi/51qemf/6y6yZOr7pVXGgrfDpe6dhxaZpW7Xr/0kgdPeq91kykHOGM5w5plgQf1aGvbJ++TtOw9lqk0a5Kto9csbZ51VnNfWqapBTZcv/lmzXF0DwFwiGUK2fAscaSHZjLA/3vveZD1/PO+nDxAlpWrz2u0T7u3IEuXe5P95blvMyGmzRmdyCs6h/XPX/PIvG4kKMgwxgAmB/sbKPLOz7XMIm6Z8w/JFUUE/PGAibynGjYXkdP0RkS2F5HjzD2Xl4sIQOsuEZkiIiwvDmtITlALiQgmudlF5NAYaLVlMCwX5lFJxeqokiQloIiJBJQlCtan3jdL60UVEn8oo25WnUSTplvbztPii0IPDthQ9JZbqu7vf08HWRBSXpUoNJI0KWNjUoBT8gs7bacpG5E+/bSv+8orDWBBuaWr98k8pZNMr7ii7m65pZleso7e0z/ASI+70Xyb2nHz7NiYlSVCAl8ZEqnDRqTQwkKXDPCO1YkNSXUplTz7G6CN3mt76kBbx65ttFwtkXo/kLTIhNt2Mhl4hUHPYaVYsmbf0m0Ux43n3Mr1x1Fbu41NHDP3Nq5VHPtRs/P6x3d2YzV+clc31sYFd3G6Q/m4RX/qmuJie7pxGhf/mRuncYV93TgbF2rs8D1ukZ+4/qhtNdX2pLY910vu1YjL7ePGaUzWs/fL/tKNM3HsKgc4jf3tl9unP6+/bKX93DiNy//KjdO48v5unIljVzvAabT5XCstUsvDTONqRU/Hp6nyQ2rHyPVSe6dHbauppaF5aamtp2PX1PTdNMYkHTOupvHzPCy/9pnbflfazxV55+daehG3zHmH5opt6G+RArKOTUwZk0TkMhEZJSJLi8grIvLxRJ0hvU1OULbzrDJbTwLIGohqGljdpJJMgiyUqsZ2Pln0TF1VuAPjpBm4eL48yMKa8uyzNXfnndXIAb4VXeVTy6FhedF73ZBUrT1a36bU1e0i2Ccr+TWipUs7pW1ptLrWswNbldt8wB3AiGW/tEC/FuSwnxggSx3NGePbb3trFEAVWknACF0dz7nn1tw993ggS57KlJRgAZ3yk8zT3wDtAcmUFwltJsSmOaKDN1nzVFZZP0sBZAWQ1QSSDBhRwEZqQWB0bQGIAToRLQta7HUS+FgayTJ7b+spuNLU9N2tIGvpcw91eWKbOSXPcuFJIrJj/8stcrOIfNHcD/llchJa2HCwt4icb+5bXgaQVURF5WujSlprWx8cVZ6qaAFZqmy1fjJVBZvMb3cPH0QNXAP4vNN0zU2dioXF39t6Wp+Uvq1CT6NJHlYeLFm6Uaelodcsc+kyIf5b9vzCJF1t04ovLdf0vPPqjq0U8gQAIcAoCfK0LX3aMb//vj9GSMv9pqQeZLFbPrSgmQzK+xVX1NxNN80MsrQPUq2rNPT3Ye+VL55fsr7Wa5e2mRBbzhclFwx6DgsgK4CsALKM5a0DlqylzznU5Ylt5pQ5RWR6bKFSx/dVE/MJy4NnxnmfEpGXReSTiTpDdnueiLwuIpXYpLaziJwtIo/GPllXiogFXS0ZCyCrnToqXp4EJgqyNB8FqUoU0MN1VtD6WXXSymhnlTHXgDqUNCmHQM+YoZatdB5oY0FAGk2tA4DK2pBUNyJlTy42D33yyUafSbo6HvLzBIBOml9UWlvAHvXZRDQt6HPSMpZTsVwpLwBKjtJBhpxLCC1o2mDHA8C68spmkEU5ctW+lLbS0Hy9p5w8PlhotySrbdLSNhNiy/mixIJS5rAAsgLICiCrkyBrUbfUXw/LFXPMKWwr9Uz8leGv47nkIBH5Tnw9m4hMjLdwAMt8r8T5ZvhIBZCVpoLKyUsqSO4BUyhVIkGVJgCM8qygdbPqpJUlFTd1OBQaJQ1wAGRhgWnn45MFsqCp/bAh6fTprceCwzhADEsQAAcneA1KQ+81Jb9VmdYBeAB07EHMWtYqZfPQVpYvBT+0pW8A1T/+0QBZyItlQrbAYMznntsYh/Zn+b777po77zwPsnjWOh79PehvQttqv0m505a+k4DOtmt3nWNCHL5JaQA9B5AVQFYAWR0GWWcf5pbKEXtlThnA9JOvagBZ7dRR8XKUoQVOXAOmVKlCGUVr89v1Rl1Vzu3qanlafYABSvqll/DH8n5G3KO8WwX41vGk0dS8adOyNyTVjUjxbXr6af+VofapNPReU/JblWkdNjUFZKU5n2udZHr55a0d5S240X2x2B1f+YB/QBZ5kyfXHbSSwfKN0zvO77Szz1F/DyrbJA3LB2X8hvx5kzP3l2zb6r5XJsQAsgLICiCrgyBrqUXdkmcenivOKnMK2y6skQ8elVMrgKxWamjw+RaUQA0lqiBLFSpK2Oa365W6quTb1aXcKnlbn13eAVWPPVZzDz7orSso/yw/H8+nV+xpPGhfbEjKMmCrgA+UHgyNxUstSdq+Vbu0Pm1dNjUFZLXysbJ19frGG9P31aIvC25YGsRB3vrOIQ9AFhuUTprE+YQzj9mOiT3EAFk4zNvnqHIlTQvJ3xHWUEBWq/ppNJJ5s8qE2G6WCyArgKwAsjoNso5wS57ZPnbznHKriCwgIp8QkZdE5MF4XbLd/FJKeQBZLrJEvPOOBxpJZZR2T10sQDbiFA1I0ahK2ipCrtkTyypv6tl8+iNPA8tTjz5aiw4yBrw8/jj31Sjl+pFHqlHkmvjYY9Uo6r2mtCWecELNXX213+H94Ydr0Zl67EZOAADim8V+WVibiK+/3ljaZGnx2GNrbuLEmpswoerGj6+6I46suqOOrkXxyKOq7i9/qbpnn626++7zFh8FGaSXXlp3xx3n+q1XgI37H/B+SmzWqVH75h4rkQZkZ+O//lV1lUq1P+/VVysRiHn55UbfjbbpVrq77ko/hueZZ6ruwQerkSM/AAswRVTAg1WKjU8vuLDqrriy6v74x5o755zGc3rgwVq0N5Z9lsgSkEXKM9cy5I5s2egUoJiMV1xRdTvtVOnPv/32ijv44Ir74x/r/THtzEQde1razRPiQCa2ALICyAogq8Mg6y9HuCVzxG6eU9hGnsBOpwfG12woOiQhgCwXKXd2O1ell6aUbN5zz9WiY1JeeKHmiIAUAJcCLJaWUMYWTNFeFSupDfRL1PqWj6eeqrnbbqs5ABGKfdq0anQEDvfEBx4A0FSja+6nTuV4nMa9tqPtlHtrbrPNam7/A2pRG0AEZ+qxUSYByxag7Pnna+7ll+tRxPqi2zEAKnbeueYOOrjmDjqo6n7/+6r7zW9Ia1Hce++a236HqnvyyUoE3gAPOjbSDTesu/nnZwzeF4uNS6dMqbnLL/dyVNCqfbO1BEBLgwVYfX1V9957bDvhQVa1WnXTp1fc6adXI98p7VfbsgyqG4dqHim8YP1KPpMLLqi6P/+5GlmpWIZ8cQbA01uPsCRxuDQ7vJ97btWdd17FTZhQc9dc458RX2tyDTi2z5L+AVmMy/YHfQAcHwKkxbFjK06k4vjdUT5hQsVtu23FHXJIzR16aD2Kp53W/JuyY0y77uYJcSATXwBZAWQFkNVhkHXGEW7JHLGb5xS86PkS8AazJ0QAWWmaoUN5gApAFktB7QJK88knOb+uUZcNOO0SFWALWgqalKaCLL23KXSpD5CwihmL0EMPeVrUpxzwooHlPbvEBw1iWsDBfJdd6m6vvbAQVR1AxdZF2T/+hHfu1vaMi/ERTj657vbZxwNCeGSZDAue8gtg2uH7Hujh2A5fNqy4onMidXf++X7bBpYKAZHUxVqkdGjDNQALYKHBlgMIAS20IyBbrE9nnOGd+MmjvrZhbNSnnQ1sHwHI4otHDTy7E0+sulNOAch5eWLdw/Ed2QOIaMP5i3xt+Prr/nBtlRPj5mMCovYPbWQNyALYWpAFiH3nnUb/yoemiyxSdSLVCLiSN358xe29dyX6wlDrDDTt5glxyEHWbGE38cHuJh7ad/9vqMg7P9eSi7olTz8yVyxCfyDv+mDqst08oOqEmAgHKF4yGIIDaTvSLVlYnbBEAbLs0lQrpQWAAmSxvKMBJYnFB1oEFChf71kAg7K1ilXb2pT6CrKoj7K/5Zaae/Y5AKDSbiho6CnIoi1tSPXa0uaag5MBWVijZsyYGWQxDsbG7uUaWNqaMcP3/atf1d1JJzVAFsDEOoIjR0DWrbd60KEWMGixAjrvvICsmoPOiy8CoHwKyAIAWUDC2FiW5bkgc0/Dp1wDrmij4IgxP/54NbJk6Q7u0FOa1CUqKIsIOqyYHmQpQCL/5Zer7oQTvCULJ3qsYGxCyjPnubKnGCALkEne9Bf8eHWPLPpRaxbLmTbwdSEbvypflOnxPLaeXrO5KTIDZJ1+hqd16KEVt9/+FQdwLBq6eUIcyPxViiUrgKxwNNEI+A0UeecBWUv8+chcsQj9gbzrg6m7bkrjtLyUaoPPGukgC4WoIAv/p3YBUJMEIlhHAAzQIqBAUcYsK2kgLw/IsktsgAxAFhYU/epP6ZACPhRkAcKgryArra8JE+puzz3r7gc/8MfoeH+mhqLGmgKogSb0CQAHxobFBoCGc7fvGysWAKQB+gAcgKyrr/agw+6k/s47ACwPssZ9wy+vQhfwAshirNon/cI/AA5+sJYRbDngiggPOu6HHvIgS61V1CciE+ppm4hY/Eed5e2WEyzBArLwscJni3ZYLqHBBwM331xz7CwPbX4zTzzhx6sADjmyJIjv21tvNYMs9sm6/voGyII35KRtLW9cn312A2T99neeFsuzBx5UiXy4kvXz3nfzhDiQWS2ArO63oAQrV3c8oyLvfASyTj3KLZEjFqE/kHd9MHWnpjROy0upNviskQ6y2JlcQRZAol1A4QKyULY2oKQtqPBbJDTqoJDTgI+l4QGSBxvUZxdxQBa0FPgoHVKAFeBLU+jbaGmTv8ce9Uhp//jHNXf66R4EQkeDX/Zs3pSUfgFDLH0BshgnbeAJ0IG1TcdFuuOO7AXlQYc9YmbaNA+yRs1Vc4su5n2toIuzNyALIGl5UVkAVrRMy+mHvtXPivEDOO+734Msrce4uNalRa1vy2mLVcrurXXVVdXI1+rmm/22DPhBATaxHGG9Ov8C7ycHHax++L2xDQY8wxu/A/zcJk2qRkuYKl/Sm26qussua4AseKK+WutsXa53263uZp+z5mS2qttxR/zP6u5Xv+LDg4p7663G7yvZrt19N0+IA5nVAsjqDgUegFT3P4ci73y0XHjKUW7JHLEI/YG860XqcpbPL+Kt438uIhr/ICIPFyFYpM1IB1k4WeNwjTLH18Yq4DRFhbUGkGWtVNTjizgcuTVgySJqgC4KMitQbi1ZD02rufvv9yAGsKNl1IOeAiy1aMETZZ5Oc1/wBkhi64SjjqpFDuvUt+Nl6QxgpHThlfLnn6+7006rud1392CAPLYQAHBqfzquvfauuZNOwrG+eUPSSZM8yNpkE2+ZwcEekAVYA2Th86QB+gAWUr4U5ItHrokEgBEgi77hAdkgg7vvrrqzz262HNEGKxH1oEm7JKDhKB42EyVQH4f3m2/mq03vQwUPLEEiF/yv8Pt64gkva+TAM2KjUQKADtAEjxy6fe+9zfzA41//2siDr6wjiNZcq+5WW63mPvWpqtvkm/iI1dxPf4qMK7GPWPNzjpjI8acbJ8Qi81cAWd2v3AMA645nVOSdn2uJRd2SJx+VKxahX+SdH0ib9UXk9/HxOKQaAVvLD4TQYOqOZJCFkkbRsxTEV4IoelXkrfQUdVnCoq0NWDugpflYeAAQGqCrZZqXTClX8IZv1m23VSPHcOoBJKBJoB7lHnh5uih7yumHqCBF+7jlFg+yUOgXX1xzO+1Udf/8Z0PZUw/fqzfe8JYfAIUGgNe++9XcEUc0gAigA/ACL/Sl4cADa+6ww7DuYPFr5J94ogdZx5/gQdbZZ3uQBa8cFo2Plgbln3usNchbASZ5jBtwQoAHeAHU4AuGFc0GaGFt0uU4tYDZOldcUY82EyUPEM1SIf5d+KP9+c/eUgV4ApzxxSAgDL4YO8/4rrsAlZ5/eAFk0S/Ll9ff0MwP/lx8AalAT61klh+95hnMNVfd7bhTza25Vs194QvVyF9s113ZLsKDLGRRJJQ0Ib4vIlnxn/ExGoOZojLbBpDVHQo8AKnufw5F3vm5lljMLXnS0bliEfqZL3eJhUuWSGvApEYyyELxAozw+QFgoehRnFmBz/j5GhElaoPSUmWuIEvrkbajTbmCLADQ5Mkoeq+8UbgK2qAFfRSs0gdkaTl8JUHWn/9cd7/4ha/P5qOALPbUsgGLFU7YAABLGx622abmLrpIQZ5fKgR4wLMFWeyj9ctfYu1r3pD0gAOcm2MOFy3LzT5Hze25J8DWyxCAYr8itDRZngNkffBBY4lNAR6861IgVkPOBrz44uYxAUbxm6IegSW/pMM4W1OwmSgBcAXIAmwhh1NPrbmbJ/vfBXyxfHvGGYAkn0d6y63+kG3as6wIcCK8+GI1WjJkDBqwmNIeXzCeHYCM8aSFW28FkHor4qab1t2SS7LlRl+0JMuSo/qJpbVtl1fShKjbz2TNO3nqZLXPLAsgq/uVewBg3fGMirzzEcg68Wi3ZI5YhH7my11i4Qoickq8hcNkEdFYYhetSY1kkIUyROkBWFgqBDxZwJCmqFi60uUrW44CBrCpgkX5ovjJJ+QBWdRRkPXKKx5kAZ4IWGr0mnoAKmvFwEdMy6nPOLRv7vffv+6OP97zgpM5/j1scqmB+vCP8qcdtLU9PkdbblmL9rSiPvLCIkQbeLEyO/fcWvT1IqBJd3KnzfbbO7f44s499VTdrbxyzX3py34vLsr4Eo8lWA2Wd3gAZL39tgdZ9EffCpqoC4hi7NdeW2saE/QASpQrj4BV2tvAcuUFF/g8lgmxVNEP4fzza+6iixu8YQUE4MCX8nnDDX5PnjpM/gAAIABJREFULOpjKVQrG/t44fzOcrQGQDogi+VlnikgS8eidTQ98CAPspDlT39ad/PMU3U33OhB1g03eCCovzdtkzctaULkS+h2IU+ddjRalgeQ1R0KPACp7n8ORd75CGQdf7RbMkcsQr/li11yAf5Xu4vI2iKylokld5NObiSDLBQdgAllidM7gEtBTitlBRBj/yZVwrYefk/QJEATkKXKnfoKWmwbe00d+ifF54dP/TWQD5CgjKjH4mg5oMtuQaEggHJABf5Y113neYPOAQdU3f/9XwNMAD4AWWpVAWTpkhYbawKyFCzQtwIVeGGMpASADl8YPvWUd37XMW+wgXNf+YqL/Jy23bbm5p4Her4NS3BEDZYeeXwAAOgkwBN9W7kColgS5Mu9a65pyIz6jItyDcn25LO0ifM7NNlcFMd3DddfX4v80XSc+KbdcUcDZPFcrrvO+2hBG9BEnwTa0B6Qqvfw4jck9RY1XVqMKiT+4L+20EJetkcf7b/MPOfcPrfd9tWIB8ac1T5Brum2wxMiX0cfnz7jlJubF2RtPGpr1x/n3MptbOKYebZz/XHe7d2YOI796I7OxnGjf+xaxsV/5salxWV/6caZOGbN3zmNY9f4jbNxzBf+4NLiRusc7Gzc8GuHOY1fX+9Qp3GDDQ93Nm64wWFO49e/eoizUfOjdP1D3YYaDW362GCjw1tG5YHU0ra8cm37svyt940jnY223sZfOtC1iv28rn+o2/iLBzbFMWv93mm07ZPjsDSS1xute4jTaMeVvLZ9a31NN/7yQa4/mrHYMSav1x97hGsVbd8bfeVgZ6M+f9L1vnVUf0yOq8g7P9fii7kljxufKxahX+5s0Joax+gMWxjJIIsv5fwGkyhib9UCrChgaNJKsdLkS0TapAUUnn59h8JGAStQgaYCjrS25LG0pSCLjT2nPdxQ9pQBjhSAAKiUNm0BdJRrHwoKKHvoIQ+y2HiTfOphrcGBGroEwBUgSwEC1hW1sJxwQt3xRSL+WrRn+UuX3LQfUsK993qQxTYIWIjU2rbccs5ttZVzDzzgj4Jh7yeW6QhYsdh5nZCkRx7LlRzVQ4A/QJb2Rx5gA4fwSy6puRtvbMiMMv0iUOuT0l7HRh3GDcjCz4qlwvvv9zSQNU7tp55WizYL5etLfLRYUuQ5UY4sAVIAdJaK+Q3YZ4A8br+9AbKwAgKyAHZYDbM2IQVgfeMbfqznn+9B1tFH97mttqpEO/0DdunPjoXx5AkdmBA/JyJHiciLInKLiPx0KCa1ALICyLJgRwEWaQBZBjyvf6gr8s4DspY6dnyuWIT+UMwR9MHXhD+Od33n/EKNQ9L/SAVZLNWgXHUpCWXsLTkNoJJUVrQBZLHclhZQ3h6oeAWMsqUNgWtV9GltyaMc5f3f/1Yj358XXmhYmigDYEAPJ3BAllXmtFMQprQUkLGnE1sB0JY2tMXfi01JWb4ioPDhnTYE6gKQ6BdfrgkT8Vnz98hMwRh1tR+uWdrCksWXdYAs73vk3DzzuIgOeTiKA7KOPNL3BUABfBHgT3mIMiLevGUMnjxo8nW1nPHgQ3XBBX4jVAWOlPNMACNW9tanizpYHwFZU6Z4fyw2IyUw/ukv+O0uWOZkx3y+LPzb36rRci18sgXFjTfVImsbQB2fNg30ye+FJUOej/4GWIJEBlmbkOIfhz8WS4bQuesuQFbd/XKfPrfppn2RpZPfASBLAa/2myctaULE1eF3IvKkiNwZA6sZQzJxxZ0EkBVAVgBZjd9AJyxZS/1pvMsTS5pTOjJ9vCAiyTi9Iz2lEB2pICsJiLAGKOhS8JJUVlgq8A+yy0+2DiAAGlhVFHgosFEFa+snr1GmKOM33/Qg6+9/bwZZ8Ai4wYLlQZYHSApMFIQpXXigDDDDGXcE8rBEsfwGyFIrCwofxa6BeoAMnKtZarzqKv81IGcFMn5rRdN+aAvoAGSxRxSAChDw5pv+y8Lx49lmwW+Z8elP19x3v+vBDx8c8IUhAX6JNmBdZPmRfvF3UuuY1gFEAbLOOos+/VmGlMEXzxnrkQVZtFe/KeoBjgBZfJnIcTrUJ9AWH6sLL2SfMP8FIrvmwwfyhz5WTTYnZSkVGeou9/RHRH6ALFLGRR6yvPZaHNdnHkvUsXOODxUAVXwVSpsZM7wMd96lz33r230R4IUegB8gO9BQ0oRYF5HbRGQ5M7UM2dxFnwFkNRSsXYrj2i5H2eUmrm1Z07JSWC7sXypk6S8pN3tvwZ0uE2rav1TIsuEsvlyYB2BRp6Q5xUwlPXI5UkEWCo6lPRSVggQAEkqL+7SAYgZkoWBbBb6YY3lLaQCylD7KMitQDshiaezWWxtKmXxf5pUyYAurldJjDET6shYmn+f3t1LHbsARIAtrz89+5veBgieWArFUaYA2YIT9nwBZWJuQDw7ogAztm/o6Pq7J32mnWuTbBKBi+4epUz1A4FBl8vBpGzu25hZfwtPhy07yoWNpWV44l/C116oxaGrwSR2WSt95p+pOO60abUiqlizkBFCCJ8svciIf+RCQCSDrpJOqjoOhNeBUDoAB6Fx6qXeOv/NOLz++SIVXxscXh3w4wW9Kvy7VPnmegKynn/Z+XORPnlxzF17oQZYFq9ov6Q929ts38DxpU6k4N/vszm26WZ8b942+/sOqeRZZ+2xZmva6pAlxUxG5IN7r71QR+Xr8D+OQzY4BZAWQZcFOWC4s3ydr6WPGuzyxpDmlI3PHDiKSFjvSWZLoSAVZ6qSOokVZEgBdWBy4t0pZlROf5wOyUOqtAktP7LllFTiKlPs0mpYO5dR78MGqY5sFAnkaKQP4EK3vmAITUsq0H+qzfAdIuv9+P0Z4ByRS55hj6u63v/X96Eaklh8U/Fln1aKd4lmawtLFmYf4QNlAP0QNe++NbxObdHrr2BVXeJB1ww0eZAFI+NqRJUP6xdcJkIVFKk328MoSHuDTgiPtD/li/Tvl1Gq0NxW8EgA86sivMiEfXqED2NLAodVYsVjmJFAf0MRSHLvBA8KITz/t+cRiB6/IhKU/6hKVJu21T6yFLEWqjPC3AxBmgaPVV6+7z3/e86d0Fl3UufW/VolA1ksveXqAbfptBdZ0fMm05AlxPhHZVkQmici/ReREERmTnGs6cR9AVgBZAWQ1fgOlLxcutphb+v8m5IolzymlThfHiohG/hvE3H5xqT1kEBuJIAtlh1UGSwXXqvwAXmxAmaboUVIoRXxsshQaNKmjS07QQvHShyrLpMLTe8oBCPhLQYNAnm2nIEuXwHwbX4c+KKdPbYtTNsuC+qk/QAbFTGBvKMoAEroRaVQQ/4FvzsljmY+A1YZlO/qwgX61T/IPPrjmDjmksSHpccd5kIUDPmAKIHvRRR5k4S+G8zf5gDdLR/tgjFjQHnnEW9E0X1P4fOONqjvlFP+Vn4Isu7SYlKMtgw7bMuD0/uSTHmRhBQO8aKogCzny/AFZAFb2AwMQU5ffhz4r2x9fF153XQNkAdqOP95vaKpjsCnPY445vR8d+Urzy192brXVq+4bm/S555/39Pzysn/ulka76w5OiPiU7hZvRZMx85RTFEBWQ8GG5cID+78sDI7vRzW+GI2/HC3yzs8FyJo4IVcsQr+cWWDgVD4mIlcOvFmxFiMBZKGkUFxYNYgoSkAWlg4AApGAFYpjdlD0mmeVFQCM7QTSQJYqVaxEABG19kAHEIAyRPlzT0ofRPLZPgLggUP1jBkVd9WkPvfyy5UIqOEE39fnfbPoAwuWd3CvGnoN0AMAsvydeGLN7bOPt1zRnrbQIAAQAFk4dGORwafI8+RpsM/Tt75VjZa2oMky6EMPVdy//+0VvI4FwAfg0DEdd1zV7bUXm53i0M7Go3U355zeQnfTzdVoufDxJ2puzlE1t9de3p8Ka486lNMX8R//8A7m0H/vvYqbOrXStF+Wypy+X3ut4o4/vhId1ozfGDK31iqtq89Tfw/aF8t3v/tDxeEHx7h4fnpmIuPioOZzz/UgClqALJ414wP88QEBfmEaqMOyJSnWP7aFmD7d1+OoncMPr0RfKQIwk/Hyy70/1lln+ecKDcLmmzs3euGa+/a3+9ztt/dFX0PyRSRLuRz9M5AwK02IWTNbAFkBZAVLVuM30AlL1jITJrg8cVaaU0bFX+tkzS2llQ0XyEJxqPJIUw7tytPatMrT7RlQRhpZRvMgoQGosEbgU6X5SXp6ziFK1wbqawQ0YQ15663GshMKH2ADGCDFnwewAyDCPwmfHiIWrMsv73OnnArI6osUOcocxe9BmqeDcuce8AEv9K0BywtRAwCLpTsFQPhjwQeB5cBddvG7pHvLnt//CnBCZElLpOIOO6wS+WG9+mrF3Xd/xQG+GBNWMcbyr39VIz8vxkM888yq2+H7VffU0963bMzYuhu9MF8oVt348dVoyfWpp2pu7bVr7otf9GCGL/See86fzUff+Bqx4aZar/7+94q7++5KBEJ1LKTIAT6mT+9zxxxTcY880hfJS8dgZaO/KVKeA31ovW9sUnHzzluJZMLYAC8AXw0sdU6e3JAr4+YDB54blqrnpwMAKxGwAlxVKh4EA6jff7/irryyEn0MgH/WuedV3d57V9xFF/mPA7DiseRIStzjJx5k8Xsj6Huy997OjRpVcxuP+a/77W/73B13VKL9sq6/vhodwaS85klLmhDzHGSfp07h+SyArIaCDZasYMnqCMgaP8EtkyOWNKcUnguyGl4VW66wXl0dLxcekdWgzLKRALLwAcICpQCEFOVMsCBFLVwoSfKTAQCGxSJZhiJXRUiKxUK3AYAGed6K5YERCpr+fV2+rPObaXKUzhNPeiDz7rteSQOIAAN2eZB2nkdPw/LpAZ3nHR8y/LFuvNHXow3gDuuNhj/8gTMJq5FlD0AH6KCcMe2wA1stVNwee3jrzn/+48/0w2IDDwAVLD4ACsYDffL5cm77HQCalehDgq98pebWWYdDjf1u6visIcef/7zuRo3ygOrOO/lCr2GdA8hxFiEgC9lBH9Cm21rQD5EyZPTUU5Xo0ORXX+2LtsCAfwuwdLw29c/E11t66Uo0Vqx60OU3Y7fqUIuXtqcOPHm+eUaV/g8iKLO/CdoARBkTzxLQzAHPd95VjS2sHBvkl22xsH3723W39NINgAU9wsSJLLvW3Nhxfe644yvulVcqkfw90G0AQuUxKy1pQvxQRB7JiI+KyEtlzldJWnlBVtiRvPt3JA/PqLPPqMg7z3LhMkdPyBWL0E++z52656BojeyUvFinOkqjOxJAFlsE6H5QqnhQXEQAkyoxAAYWHZS2zacNdSjTzUuVDqlVqFy//jqgpQGAaKt1AHjW9wilroqdek897S0jWNW0X/iyXw1qPjSTATCANYnA3lOALD0jEPqUWZDIQc0//KH3AcMyY7c2WG01LCpV9+Uve3q0Y1wsmxLgSa1qVl5s4Mk2Djiq099SS9Wj7RpOOolNUGvuyaf8dgeXXOJ9tdSSA/iCRwJfHLKEiL8TeYyLbRLs8TvUQwYAy4ceqrmTT6m6d9/1y6wRkTZ/tK+33vLgBQBz6qn+a0OeM+AlTcZKluVmeOdZAZABSAToJtuRp/1RZ9IkfwyQ1tcyhj96tHPbbReRitpoGVtJwON6G1TdqafiP4blzPfpa+f/W9KEyLmr7WJH57MAsjqrmAPw6R35Fnnn5150MbfsURNyxSL00zBJp/IWEpFvxfEzneokjW43gixVSKpc8quOmWui/FkixKpjg/ZhwQFKU321yLcKDEVOWXKLB+jYelxjscBxXYGRrUOe96ny7dgfSjf5xDKiR8zokTPwjB+ZKnAdg6WpeaTwDbChnG0bdt/dAxTK4I0yyy/9b88xLXd6ixky0LocfQPIwtr0n/942ixvAtqgDy2sWQAgK0eshoAsDn6GHv5Y+H55kOW/nOR5vPaaB1kTJrjoXESekwaOFQJkYeXDQgY4Zfnu8ccbPmXUpV+ezR13+DMB33/f73MFf+0CdYiXXeY3RwXA7LST34QU4KRfO7aiw/YOgCzAKZYqBajI18pY21ue2An+r3/1IE75IJ0+3cvkhBN8Ky3jjr7gcd11Pci65ZZhB1nziMheInJc7Ow+Z9oc08m8ALJ6BwQEQNfZZ1kEBEUg64iJbtkcsQj9Ts4NlvaWIsIuyWeKyFnxPjOb2wqdvB4OkKWKQ1NVQppqPulgAwAF5Q0gsAHafsmtYWFQIIWyTCpKBWAs91gF6uk0aKP0sYQBslh+I2gdUgtMsFChOHF8pgwL1yOP+IOqAXTaD21UgdsxUE47G8ijPulhh/mNSLXcAjDNA0xsu23VnXOO3x4BMENQhb722vhl4TPkwRogE94AIPTDMiZyg7byi4wAWZzpB1ik/Z571tzJJ3tLFktsOp6llvIO3TiQ46elgW0OAFlEliPpiyU1QJZ1MPcgy/eF8zogi2XNpFyUrk2pQ/zVr2pu9jlrbqONnFtpJb8JKR8BJJcIbVuukR1yAjTzMQDPk5D2XMi3PAFUOV6HfpQP0nPO8SDroYciUv1l3HlZ1twX1vaO9Bde5JdxLV3fqv3fkiZE9sj6awywLheRYzo5V6XRDiCrs4o5AJ/ekW+Rdx6QtdzhE3PFIvTT3ulO5HFAtLVefVpEyBuS0OsgCwdmu3Rn1U8SZKEcARBYWbhGgWvQpUSWxxRMUIaC03uuaUPKQdJ8jWfrUAZYwGKFAmePKJQ0IIU28IoTNSACPtR6RTn9az/KE/e0s4E8+gAEcZQOWyRooE/KbKD9rrtWI+d2+tAx//rX3mrCgcmApKOO8qCD8XsZeZBFe/hjTMofeT/4ARuS+p3caf/zn1fdRRfVom0W8BFTPrbZxrlFFnFu6tRa9DUivGEdA1yRd9tt+DFVovrwBhBjKVEDY6I+5xayTQJfJAJ4knLR+smUel/9as2ttVbNHXaYBziMDysV/TG2VgHHdJ4fIBBgrc8o7blAg76UL8A/IIsvPDWfdI89nJt/fpYsfa9axh28yGw1t+Za/nxFlgx5zkqzFZ9p+SVNiPhcacCK1VEnd+3IpgFk9Q4ICICus8+yyDsfgazDJrrlcsQi9O273MlrO1HRz+wikszrWP/DBbJ04k9TEJqnqdYtkvJpe6vP25MgC/psSKr7HaFklQec4rGIoUwVTFCfcr3nWkEKPmD4GNk6KEnAhSpvzsObOtUrcdrCJyACxYmip08C9FHgSYWfpsyhQx9smoo/FntTaaC9LmFqHukhh1Td7rtXoj5oT9hgg7obNZd3OMenarPNGmAKGbFsqFYv6FqQRftf/KLmjj2WLxcBa1W33/4AA2/NuuJKDzKpd+yxAJu6u+FGDon28mJZEJCFPG6/vereeMODLHibMcNbCSMm493akRdLb3yhh2/UBx/kB1mMYZ55au4nP+HcQ88LWzUAcJEvY1OZaJ+asu0FG67yO8KixzNKykHrkkLH0rrwQn/WouaTrrmmcxtu2GilZeTQx1xz16K9svhIAJD14ouVJpqNltlXJU2ISVCVvO/YvKWEA8jqrGIOwKd35FvknZ97kcXc8odOzBWL0Nf3uNPp0SJyvYjsGMdrReTITneq9LsNZFmlwvVgAu2xYmEhSgsoR5SiDbohKXkWZLE0hK9RUunSh4IsUgVZWKNYMlQalAFwUOqqvFHQzz6rQMpvL/HCix6AwAf7dmlgeU2tP5oHnTQZUY+tAgBZKH4N9K3ASPNI/3JmzW25ZaXf8kbeZz5Tdyus6GXzve/V3ejRHlTRJxa4Z59rACWsSfqxgNI99NCaO+igmjvmGIBLNfqCEQvU6WfU3AUXNmT+4IMe2EyYgJO755VnxpIi1p477vBAAiBFUL8sfW48D5YIcagHpHnfKIBwow/lKS1lzyr4O+cc+nNu9jnqbq+9G+AK+ow5LTzxRD3q04N1D7IAQq3q86zs82L7B7Zx0Hye1RxzOPeb3zR60zJyAO7zf7Tqll3OW87YYmPKlGEFWTUReT+O/xSRqrkmv+MhgKzeAQEB0HX2WRYBQRHIOmSiWz5HLEK/0xMEh6ryNSFhMxGZKCL/F59qv2yc3/FkqEGWVRoojqx7ygYTADVYn9T/J0krDWTphqTUBSCpwiSfr9uSIIty5dPWB+gAsvCzopx65AFIuCYfy4t+qYdfD1YsPffQg7rG+PEJA3TYoHRtHteMGysSR9fYQD78J8PNk2tuy60qDmdsAucYymx1t/XW/v5Pf/JLh4yHPgGt+EYp0GHcLNmRasDJneN19tnHuTnmrDo2KAWAsITIUT0aWBb7yEf8dhH33uvbs0wIYILenXf6r+igT1C/LORHANyx2/vpp3uQ6g+wZtuHxhmEUcUWfyZO9CCLHe8Jn/tc3a27buOZ8rzsuCyZadPq0deO/I6IgKR2y3f6W4HO44/X3RlnsLzp+775ZuTs3NVXN3qhvrbhS8tPfrLqFlrIP4dLL8U3q5jzezdOiEUmuwCyOquYA/DpHfkWeecBWSscNDFXLEK/yDs/kDac87VGSoMviAh7Zw1J6HaQpQqmoXbyX+FXA8hCUaYFwI4CBS0H3OiWB5SrgsWyhCWmHchSfknpG0DCNf0AsqDJPVYJtdbQN5YvtifAWZ5yFDZLhmp5IiWPMg1cQy8ZACTspH7aac1lCvJsfWTz+ONVt802FXf++R6YnHoqyr4e+U9RF4sbX7WdeabnDcd2QBb+WQR4AOxYWV5wQc3tuGPNbbVV3S20kHespy6+U6ed1gBZ5K2/ft2tvnrjkGi+FMRqSLjnHr9XloIs+qJv3Y2dfI6YOeMMzkHE2ub9stglP0/YcquaGz26UXfXXetu3nl5Xr41z7/V7wdQiO8dAItnAZgHLNtnlOTBlvFbA2QpwDvkEA+y3nmn0Yr62ubKK2tuscWr0U75yAEr3+mnV6K9whot8l1144RYZMILIKt3QEAAdJ19lkXe+VkdZD2WMan0rE+WVRqqDlSJcJ+8tvdaP2+KVUj9otLaoKgsMKCObkiqfCrIwg+JDSpbgSzqU9fyi+WLSB4gBJCj5fhM3X13QyEDusgDSBHoB5DFbvUEaFNmFT60GEMyQGunndiNvFFG3TSQRR7gbt99K+7wwz3Y2Hprb8lSx30AB35Lu/3IAx+ADcfiqM+Yjg9LlY4P53a+MGRH9+VXqLobbvRfwl11Vc0df3wzyNp337qbfQ52vPdWMvyxdIn3gQfY8sEvj+k4ATY8V8YOqAEkcrYgsuJ5+uN4GsBJ26Wliy9ec5tt1qh70skeYE6d6mvTR/KZU8LzwOkd4K1jxrqGpS0rUFfrw+tZZ1WjrS5os8km9ejrRtte65J34sk1t+JKWN4AmVhKOa+x4t58M0aEtmGb6yITbsZ8NWxFmSBr9i3dRnHceNTWTuPYBXZyNo5b5CeuPy79CzcujmNX/7Vriqsd4MbGccyav3NN8XO/c2PiaHdeX3/sEc7Gr6/X2KF9w/hMOU032Ohwp/FrY45wreL//L+jnMYvf2+80/g//3u0s3HdzY52Gr/6naOcjbbeV757tNOodDVdf9yRTqNtM9O14Wm9bxzpbPzy1uOdRsuD3Z2c669+O1+07dbeboKz8UvbTnAatU9SHZ+mOj7SdbYY3xy3HO/WyRGVFmmShvJA+pXNx/fHpNxsOyuzdTc92tnYNBZDD9qW5nqbHOk0WllzXeSdj0DWgRPdCjliEfqdnjiey+ggqyyj2cCLhsOSlZz/rSJJXtv7ZLt29yhiBQppdVGg7Ppt+1ALkipWlCmWCgAPZwySbwM0aO9pNZcBFLBm0QfAhOU6Df7MO6+QaQ+vbJpqlwSxqKFMNcCbpaH9armmWMiwImF90wB/ACrGYwP9YRk6+eSK+9GP/BLdiivVI58sKxe+wPv85xVkeV836zMGiCRqG5a2AFksbbENxMMPs4RXdxxYDcjScVDfn9VXc8ef4B32AVkqZ9pNmeKP8VG+AZEAQ+QKqKEvQBYAlD7w0eILw3aBA68BLEcd5f2iaM8Grljxjj/et4Y/eEF+NrDEB8jCR0zHjGVPlzFtXXtNXa1POmlSNfKfw0C54IJ8lWlrN/7p4DkdcaSXJTzjy4ZFEZD12GMBZKVaQQLICiArBngWEHEdQFY+rDD3wou5Ff8wMVfsRpB1nojsmjLUnUWE/WeGJHQTyLIKCFWTvG9WP9l3KEwADkuGrYIHRg0lST3dD0v9ZFDaACRAFptOqvKnLvyp8iXVa+2PHcPxY0L54qujbanHkpha2bAAwSvgQbdtgAb+WrqUxD1lttz2r32Ssov7brt5EKL59AnISvKIfNib69bbqtGGoXwxN9fcdfe1DZrlontJoeyhA6+6rEofyBs5wRMBwLj9DjU32+w1N24clhe/3cBtt9Uch1azNxgBfiIfMKm7H+1ejzYfZRNTDU88UXV33eVBltLmOQAM+ZIQGQMqzz/f0+N58XVhni8MWdIEsEAf2jxzQO3o0ewdphx4qyJ0bWCbD3zqADrKF8+Z+3ZB65PyxSU+avhn+SXa5n60LrI++JCaGzPWW7KuuML3ctZZFXfzzQFkBZDVbNHAuqFWLNKkVcNaPywAseCDa7Vikdo2M10HS1aTNatnLFmArN9PzBW7EWSxy/vdInKriEyI420ico+IjB4ShCUiQwmyUBiqNKwi0nxNk2X2Pu81/jEAF7WYpLVDcSYtWQAhAJUu2wECsJaQh5VCgRL04FdBC7T0WvsCeACy3nkHy4q3ElHGshbWGqwgBIACvLLRp4I78lHk9KsKnrEoX1HDFhtf/uHAmjv88Oav3KABOErKH2sbe3Nh8WNX9sMP98r+gAMa4IG+Lr3UAxKcs6GD7xi8qTwYO0BSZcCYttjCt+EMROoBQO673++VxR5TBOoTl1uu7v7nq14udlPSZ56tRrICwKkcAKWALBzeeSY33FBzV17ZoIdmcSL8AAAgAElEQVQVCwDGs80K+K1x4DJyRS48FzYH3Wwz55ZZptGS50i0ATAGyLKO7lznBVn6WwdEs1/WMcd4uT/2WHM/+rzuu6/uOGdyi608yDruOM8Nh0Ofe97AvzDsxgmxyJwXlgvDcmFYLmwsE+tSIWkSWBd55yNL1u8muhVzxCL0i7zzRdpsICI/jeOGRQgMpk23gSyryLhWJZPMb3cPgGm1Cam2VWBk++Aa8KD+RgAABRTWGqW8UU4baFk62gcO3BwWbR2iWabCkgU9gvKKgragUK1qar1C0QOyFGzQVvvX/mi/6641h5XG1qMt4CgZ8CkCsFD3l7+sufXWBxixFNYMsrBcYfU56CBPB0d85KSgj7EzHgU23H/7O77N73/vPz5gufXRR6uR4zt7hBHolzFsv33dLbAAm482zpmE1owZ1UhWgCkLdABizz3nQRYO4YA/ArT4whCQBRjLCl/6kj+4mjrwi3UNoDV+PNtK8JWlbw2PCiaVHkuxgCx1eicfIMgzhIesQF8aecaArM03r7uPfaz52UKDeoSrr2b3/pr7/o5VN8ec+ND5HlhOZamX39dAQjdPiAOZzwLICiArgKzOgax5Fl7MrfSbiblir8wpOv+cLiJvioh1nv+EiNwoIs/G6YJaOSsdapDVShGo0kmWq5JJ5re7z9qEVNuqgk/2wdIMX34RKFPfKhyVraKnTEEOtNICFqKnn/ZLZVrOLt/4ZKnixjkesANg0a8JtW+W73Rpjf6SdbR/pQ14wSLFMpTllb4sgNP6OJHjcE9gGW/55Wtuno+g7JtBFu2XXa7mvvnNBo98DKByoj0gwwKbb37Lg6zzzvfAgw8HnnqqGoEK9sRiPMiN9MQTseT4LR7UrwlaHLYNmOHrS5UXfSGzx5/gUGa/VDhlipc/8oAPQBY+Yq0CZzHOOcpvM0EdxstYALR33eVB1mWX+dbQpG/41ADARMbkaaS/JAjW+jbV+krv4otrbqmlam7s2OY+aEMdZHT22XX3p2Pxtas6nPXZKZ+AfABZ06cPbMmwCybEUuawALICyAogq4Mga/RibuVfT8wVc8wp40TkaRHB53y/DFzCsYI46rPTwrCF9URkzQTIOsowzgBybWjaiyALxZS1CalXT96KklR4lLGUZZ26AUpYpAAtFrjQFgVMbAWysII9+pj3EdJ+WfphGRHF7Z3q/dIhCtrSV17Y/FOD3yagcU/f8KEBiwebkGI1srQAbxbAaX0cyNU37MYbvTVptdWaaUIfXrffnn2aGlYdv3dYo29ABgBHw9e/7kHWY4/5PIAkgI5d4O+4w/ehcnv0UYBNzf36Nw1wB72//70aOZgnv+wE+E6bVnXvvVeJnN4fecTzAa/wgGVHLYXKj03x46I/jvohUJdtIRjnhx86N9dcLtrjS9v4Z9UYK9Y/llnpTyMgnGdI3ayg9UkJk66uRcflHHhgs9wpow5Lk1i7TjnFg6x116259dbzPSA/nN/vuWeWA1mlzGEBZAWQFUBWh0HWARPdyjliG5A1h4g8LyLLiMhc8dGBq6QgqI+KyO0iMmW4QRa8LZUAWSDEhWOmSblvG4YKZKli8aph5r/J8htuqLq//rXqzj67EsWTTq64+++vRIoQZYg/Ds7ULLXx3/zUqf48t/vv9+k99/RFvk70xC7qbM5pAQD9oaBIOR9vzz2JtSjuvHPV7fD9qvt+HDfbrOK++a2K23wLHzfdrOL+3/9W3Hf+X19//O7mFbfV99gXqua+un7NrfMVH9f+UtWtvkaf++znKu7za9bc5z5fdWusUXF77NHnDjqozx1ySJ875NCKY/du9nr67W9r0bLRfPPXHZGtE7C4zDtvLbr/yLxV99nPshzm92U64y8Vt822fW6HHSpRXHGliltzLY6haXztxxj1HssP/lLEt9+uupVWqrqPfazmPvEJF/XL7ufbb892C80AAuBwwgkAE84P9OAAgHbMn2ruT3GcOLHiJkxoxGWX7XMye5/bZtuK2267itt6az/mCy+uuIsuqbh3361EIAnLFctsH/kIe0BVo6/s+NJuwQVr7sSTPMjCMgkPGt97rxqBLI6V4ctCQNuSS/ov9D7+cWRYdUssydKj55Xx8Pw1HnJIxYlUot3rAarPPFtx11xTcXffDWCpusUWr7iPf6LPbbFlJYrf3dw/KzZsJbKcyvj1d+vBtgdZCmjJI1CHDwb0mZEify0/6ST8rCrRQdVs6XDmmf7IHIAV+2ixmz11+H1wzuRW3/Nj1bfosssq7oorZjmQxdw06DksgKwAsgLI6jDI2n+iWzlHbAOy1olPtlFMsr+IEJPhjyLyrdhXfVgtWTCWnKDeS3D7buLe3v4wFsgDSyyxhM7VHU1VGeXphLqnn151V1zBUlHF3XZbnzvxRK9IFCCgZNkPiXvAFlaNZ5+tuukvVN1zz/W5Rx/tcx984BUPPjuAF5zLCdBH6SrIGj++Fh1gzNEqRBQbyl0je0j97ncVN348sc8dfHDFHXpYxR12WJ87/PAYLB3a5/74Rw4Zrrlttq25Pfaoub1/XnN77V11O+/S5364W8X9aHecy6vuS+tU3H779bmLLupzF1/c5y64oOJuuqnqJk/2IG2uuepuiy18/Nzn2ICy5saOw5G87tb4LAq5GlnHWP7jzMGdd+5zJ5xYcSecUHFzz1Nx883vQRaAQuWOZcdv1Om3PQB03XKLpzV2bNX95CfO/fjHNbfppjXHF30WZAEGADdYiwBZZ57pnyLLeCwxsi0DkQOaJ02q9Mdll+tzo0f3ueOOr0Rx75/3uR/+sM9df33FnXtuJXo+//qX5xV+jjseWXGOIuPyR/lsvHHdYflj2RQ+eGZErEbwiZUQkHX00fDm3A9+UI+2oth2W+8gftZZHuhQH3n4Jd+a+853KhGQ0nHefXfV3XRTJfLzwtdrtdU9QDz66IoDPP74x31ux536onKWWImAJZUvvHFNnv+atGHRwqKIhQufOJ4ZKfdqWdx336qT2SqR/NjhfvLkqsOh/dZbK9HZjbfdXnP33Vd1p5zq/xk49FA/1rff9s/h8ccrbtq0ngBZeeew/vlrHpnXpX5ZOFtnN3YMfQb5zmq/gTYgyOKD/ut5Ri/mVtlvYq4oIi8qrohT3lMNLAGepjcisr2IHGfuufy8iFwS5/FB4CwNsvrH1i2WLK8u/F++5vvznzlOxX9aj9K6/HK+omrsfYRyVEsAVgJ8ZNTnCP+a116runff9fWxwACysD4QLMiiLuDCHvVieUEhAibYTRxlCRjAd4e+6B8+6AegR8DKg9JH2RLwKwLg0CcBi83ee1fdscd6JQwNDqRm+QvFu/barn8piPr77MO5gnw953nnTEKWuY47DiVfc9/9biUChQCPp57yS26UcyyNyoO+vYL3fVCXcOSR3g8KwEAgHzDFOJVfUmREPscBLrCAc7vtFlV3kyax75Wn5XMaf/v6OC7HuZ/9rJF31VV+KXPKvd7HSAFOo0bz1W67sXzpokOu8WNLBj4gYNkPeW+7bd196lM824bMsf79cDc/NpYe1V+MOuzfhVwZM2PkC8WHH/F1eQ4bbOBB2pQpvtebbmKD12r/JqnKC22J9rcI8FdART2eAzRt4DerX5JusGHNrbKK30cMWjxX74PmfzfQZtn5mGOq7le/AoR5kDVpkqU4sOsiE27/pFHexWD+UYy4yLRkBaAVAGj4DfT/Boq88xHI2neiWyVHbEN/ixSQdayZSmaPrVfMCYSuBFldv1yYVw3gfwPIYokIpQMwuetuv1yCcvJKrQGy+CIMkKU+SCjTt96qRkuKXPN1GyDrihioaHsULP5XOIoDpNICgIpNJ1Gc3rHZgywPsDwAAQSyhEn/LFtdeGFDoeryFH0S2Kbh2GOrbq+9sGR4Jc9eVXy9B9gaNcr1fznGFg74V7EMyQHK0ICPOeesue99D+sGX6VVovP9sIqccQYgywMnzgpE0RPglWu/VNjg7X835cibBqCCf12OU35Jsf6QTxgzxrk11vD7YmElmjq1QS+qEP+5/37vPH7BBY3cO+/047nrLg9ELfhs1PJX9PuXv3gwccWVdffwwzP3w3YLgE6W1VZYoe6+/W3flmeO3L/2tZpbYw3/XNWKRQ0+bACITpzoxwUgvvbaWrSsTDlnEnJuo6/j+2XfL0DWlCnNvxP4JCrIAjgBiJA3kbLk/mb0QT36BbjOP3/N/WDnavT7AXgCyPjNwzPtCewNdthhFfeHP/ilYg6SPuCAqKjQnzYTYjzPdTxJgqwBz2EBZAWL0qxmURoufou88/MstJhbdZ+JuWIb+u2WCz8mIm/H1jAsYv8RkdeG25qVnKCOTji+4wjfNgylJSuvNsCyAsgCkKBkcOKePt2DLBzQvVJrgCzdT0oVEkqW5cRXX61G59oBsA480Ctj6mh7QNatt3qQBbDT9pZPlqk4o06Xm7Aw8LWfp+GVK/0BslCcl15abzrOxi/Tedq0oR5WE8AT1jF4QAmjxMlnyUs3muQLPOqx9Lj7j/14ATurrFJzK6zoj2TZckusK/+/vfMAk6M49ngrESTQM8GYIJuHAT+TMcEkI5LCGduYbHJOIskgkSWREZJAYHKGR0ZkDCJJ5BwkokDkaJJNsOGBbkO97ze9ddM7mt2dm929G1bd39c3obure2pmu/5XVV1NiAhCN4j85Cci/foVBHOZCnn64BwNiYaD4BmXWAJgYp+Fa2gDtAB/CHtN1sRmhf2xx4r07Cny5psWKGm8K62rx7POsiDrgw/0jsiMGfZ54DnACO0dPInjO/feeMOCLPb0w2QYTYAUHNcxWcK3sWNtDQVZRx2VF9PT+nsBWPSZCNYKgEKziZYJvy3AGu+WBM199inIgAE2tAL3AFH77IuPYKhN5T7jpEyfAT7zvUKXc8oATapVDDoINra29zFzM5aLL7arIeE1AIvvBq2p0gVcjxqdC8I4QHP11UU22kipdf5YY0KsOW80qELdc5gHWR5kdRdo+bH1m+Y3H4CskRNlhQS5Bv3exph3jDFLOY7vK1SZR7pdk0W0+E+MMTljzEfGGKLEL2SMmVoK4cCRkA41U1eALASFCosk4oDYP4AstDMIFIAJAAGTHpoEpaXCDY0GYIiEILVC1oKse+9tD7RYmAzPPc9uW0N7TGAItEsvLQabKXOtdN0xokEhGjd9IahxtEdoUpd7ABiOAAac8TFdUZ9EHYQldTjneXgWgCLgiYCkFtQUAy0KQBCw8NlndgRoxPbbrxjEvMK09fnnNvL5jjsWpCexko7MyyGHWjAF4FhxRZG2NhG2wPnVr+x9HSMC32pH7NgIgonWC/8e+yxWq0N9q2ELQRb8ZJyke++14OmiiyzIioIHO3KR7bYTGTBAr+yRCPY89z33WJD1/vvVQRZjwQS4zbZWm8i1mxgT3wTR6OHbQw/ZUt4l74p9EgEw+DdxrWnYsEKwoACekNn7EHMg5yTiUZ10kt3cGiCqib0d0SS5Sb8DjiT9Rhgb9Ljme1H+aVu+Ce6fdZYFWZjG4TsxvnhPfJt8OwoM0UKOGJELTIb0ddBBIn37olFUip071pgQa84bDajQkDnMgywPsn5sYKe7xpvmNw/IWnHExEQ5Af1NjTFvlFYZHlOaQ04wxmwWM590O8iKGVO6W1kDWQgQnJBZZUVSYIKWZfJkKzhVoCF0OWeJ+3vvWSFn/Yes8EaTdcGF7TJhgm2HeQhBTxsFWUcfXQxWHiotV1QhCDEVEngSYcdWLWi80BopDQVQmHMAh4AsjR1FGYKSumSAIiALWgcdBEix4IUygNfvf1+QZZYJR0D09VNPBQRY/yFWQULzwgsteNhgQ5zdLfD68EO7qfMJJ1g/LtMjH0Rxpz4CHj83V9hffbUFWQ8/HD4LPGYsFlSFoAThj9Anff21SI8eIrvtXpRbbw0BSDhqe8ZKv222Kb8LIANksVchmiz8rJQ35TUtQOXeZpuJ/HJp+x4UBLl1L7mkIFtsWRDMZ999Z0t4t2iBAL6ArNFjymNmsZgAIMo7hybfG+Cdbw1+sYfjddcV5MwzLY9UGzfpRrYesuCfnhg779L9dqzPnuUX9HjnCsrdcXPO/e23z8uCC1oa8Jn4XrSBNt+O8h26+x+YCxZjUHbttRbs6kbWUdq1rhNMiOkmlC5u5UGWB1ndBVp+bP2m+c0HIOvQibJigpyGfhdPF93TXVeBrFqTvpYjQFhpxzYuJHywACbcJ/gjK/9U8HAPAQdw0s2QKUPIktD8jD62Xa65thD4So0bz16CIaggBhP+WJiKXEEZNA4AhRXumCMRppggCRcBaKFv+kEokwBehJ0AOOk9q5mwY6E+/WECJZ1xRlHGjLHaI8ree78gCy9ckJ13DoqDPtBi3XijBT077ECcJFb9WUDJCsNllmVVoh07gAfN1P33i9x2mwUXN99sxwp4wtzkCvv99y8KqxgBPvQP36hH4pyxa+Jceco9NGarrILTeTzI+vhjCwDOOEMphMeDD7ZO72ig2IyZRP/RpPcwAfJckyezd+Ts9c6/oCArrFQIzGdKQ0EWvPrVrwoyeHCu45v59luRHr2ImG6fD63RlKl5efll+97w17LBXPnebN+6LyKLCfDL0m1/GKOCLO1bQRbfAPzEBMk3HJcw3f7yl3n5wx/s9w24ZREF4IoELX0PaMT22isXaHgpe+89y2PdXieOfrV7rTIhepDlQdaPDex013jT/ObnWWSArHjIxEQ5Df3uQT1d3GvWQBaCkdAN99xjBQ0AR4EJUb4J7QDQIVkhVxRWmWnATgSVAoJ33mmXv/61PdiWBQdtQBaR1mlHneeftyCLAJn0S3YT2iE0WQqq1JmeOtS1gM62QaCy+vH220Nw4gpJ2rACEbBDYq+9vfay/jfQefJJC4zOPz8oDhz50fqoYznL/FlZpuCDOFALLkicJ0vvqKOK0qMnJkEJtgQyPTAvWS0XGhU0ba6wX231oqyxRvi8lm/2Gt7wPPRFjoKsXXctSt++xUATZUdb/vemmywA0JV5bulxx2EiK8jNNxfkkUdsf/pMWk/75frhhy3QIUSDagi1Hu+FVZZzzU3IjPBZaA/veUfEOuvfPwRZU6daPut7YsEB5sR33rXfBdvzALLYVokVkvPMU5Thwy1tTLKArDvvtDynnziQhU8W/ANooU1DixiXWNgBWD7hBFtOiA1il7nAiucgAbKINUbsOBK4dLHFpGwj66Ag4Z9WmRA9yPIgq7tAy4+t3zS/+XkXGSAr/XViopyGfhfDne7prtkgyxWYSeZ/BNN119n96qjPijuAiRWcNn4WWgdNCFKcxj/91Ao8wAKCj/T44+0yfHi7vP12TmbOLAZxrIhFpaBh0qR8sM8fAswKzFBQ0x5HbVaakSgH3LHZM0lBFkcSwOTcc/NBYFOuqW/NPbYcQIDPlpq82AgYYU7UcMaMvximLdXu3HefNa2p9gYt1nbbhfvxocXq1SfXAQw32QSfrHD8y/6qIOsPtCALbRUaFe2bY58+ONSH9eGJPgv8AyRwzXPY85DnNvRDUZ58MmwfMKH059BDReaeG1Ove9een3WW3eiY1Xx3323b0wdZk3uNCbBXr6LssqtdCap1OMIbtIHw7bLLytsDTgCv55+Pz1NOZsyw4z/xRFuf74pE5H3ie+FrR7rggkKwh2NwIfi3FWXNNUPaRx6ZlzPPVKBjwbo7dt45vAbYwkfoqmZKaeoRTSMg6/77LT38sfiHIpez13wXtIU+GjdA1o032jJosJH1Uksptc4dW2VC9CDLg6wfG9jprvGm+c0DslYePjFRTkO/e1BPF/eaNZCFYMEk+PzzCBurvUBgqiC78SbrpKwiBSCAPxNaDgsOQjBw0815GTGCyO/tQagGBOhdd6EtsKYhHJlxcFYwoSBDaWsQTK4BUWz0jMmQRF2EuI4LoX3ueXl54cWwHAGpNAE2gCzakDAvosnCLMrz7bGHjequ2qbzzivKkUeGwv2hh/Ky9dbEDrPtieoOeGDLGjDl/PMXg0CcOh6c4/v1s6AOp2kEP2CJ9OCD1rR47bX2mjFakGVpQ0M1MZRFQRbmM0x4F18cji8gXPqzzjoi663n3gnP2YNv+HC0WHZ7HUroT8et12ELkTXWEEHzposbtIxVgTvtZEGTavy0vYIs/OTg0yWX2LFuumlBllk2/EamT2dTavtN0BYnelYrajriiKL07gNgtndYBXjggVajyJj5LnTs8Ir3DCDiyPeLJouxxKXDDstLj17Ec7Nj++67fPCeoEniCB3o4gtIVP+//92ajCnXjaw/+SSOevV7rTIhloGsntvKICcP7rO9aB66wF6iuW2Zw8TNQ1cZLZoHr3m8aB603kni5g2Gniqaf/fn8eLmdbeaIJp/t7kTgfuP42Wgk9f/03jRvN6WE8TNlehtNGisuHngpuNEs46Ho97TozuOdbY9Tdy87tanieb1tpggmt06nK+/2fiOPPD346QsO8/l9uU+B+fKT44bb3hKRx607oni5rJnjPSlvI0ey8YTabPhkFNFs/sOOHfHu0HbOHGzvh+OLn2X15y7Y3Hpce7259KLnuv4guPgU2XDUtZ3qMeNNh4rmt2xBufOd+nSc8fAeZrffACyDp4oKyfIaeh3Mdzpnu66AmRVn+7DUoQVAgqQhd8LQATwpIKMcgQiTsmauIc2grAK1AOkacKUNH58e+DngvmHbUluviUf7NUHqNh333wQ4R0aZISZJvrGVIhTPQlt10cfsSGxFbD05YIstF5sffLe++VaCKXHc+EjRj+ajj22GMS/AoytvHJRBg60K86oM3JkOYghuOrW21hfNeIwDRwIyMoHsZ7svn/0Dw8s/XPPBXzk5amn8cXC1ycEfIREACRhZiXRBn7o2DgCEMgKsrin5bffXiyBOn2S8MjGy9F9/8JSCQKYosEjLAZxtpSmHqnrnnPNSjrMdro/odIjjMQ667ACsdDhk6dlvC/eDwBnvvlyAYiF9TiZswcjiWcjjANbMaFBAuwwNvZw1ISTPrx69FF758EHrV8WoAdQ734z8AtQhOM63w8aROhyjEs436+8sg2/QTnt+E4UiMMHBWv8I7HrrnZnAAXLupH1LbfEUa9+r1UmRA+yQuCnAlmPruCPgicFWBwVYHGM1vMgKwSWcyzIOmiirJwgt8qc0nAkliWQhcDCRwYwxGbCBBklPAPCRjNL3SnHiZsE2AFkYToCYHFNAjRgSrr+hvZAg4SgY+Xfrbfm5aWXCsEG0nvskQ/2oVPa9M85CXAFyEJYkhCANuq7BSMIQhWGlLNKD/CHkz5CECFP1oT2DMd3pc99QAbR5gFQ+FMddhhC2T4z/lhE9ibRBpqAwvHjrf/WllvlpVdvtFsFufBC67c0fTpgybbBHAnIOu00NCVWeOtY/vCHovz857Ye9xRQKR84ArzoU49axj3GjRltueWUYnh84gnrj1VJ8MNTgEwYkDR8RqhoPyFFkeuus8933XXhmCnHlLvwT4vyhz+yzU347iiD93wDHAcNysmvf83m1PAk1FTxDTGeV2dYkEVcMsb2zjthP59/bvvGRErCjwq/rEcftVsPuSAL3vOd8c2gkcSxXbVaPJebCL3AvpR77W2Di1JOXbSO+o8C96AHXTSYu+2WDwLPArxIcRtZu31UO2+VCdGDLA+yXG2Te15Nq+MC0KhmyNU2ufTmSJD10wGyyoETE+VWmVN+VCArTmBWm/gRWG+9ZUEW5i0AB0BL6XDEMRiQRT0SghSQhYBCOKFdILFPIUFIn3q6PQhKCu1rriF8QD5wMsfZGZCFOQe6JBdkEUHeDYCJRgLtBaBDtViugGUVIFoOQBZCFu2UCkvqcR+hq33RH0ADMyEbAqMtIZApmjvCCQCyiGWl4wLcjB+fl733sc7/f/lLXlZYIS9LL43wlSCeFI7ZCgp5pP7987LNNtYUBkgkcX+RRYqyxZah0Fcw5fKZc+6TAWG2rQ3cCVjFjEZsqn/9Kyjq+FPLhIVvFEAGkyV01Dcq2ncHwY6VdEUZeVg4ZspZPADfTj7FRvxX8EEZ3wU+ToAsQjiwEIAQHoAs/OBILJYg0v9771kz3fXXF4LwDfq8QSURWXrpovz5zyEPhg3LyxVXEE4j/HaoCyCCX1abZbWSfAdc8924iWj9jAWtLWCPdnzDP/xgHd+VH0pv2jQLsgheqiZl6K29dmXTrNtf9LxVJkQPsjzIcoGQe+5BVrmpOs1vfl5A1gETE+U09BsOaLJIsJmaLBUU0Qm+0jWCCC0TIAoBCeAIQUMItq6+OtyHEAEGyPr3v0OncOgT2gCQ9eWXrMDDJJMP4joBstiE94wz8NcqF5QuyEIr5O6XB8ACaFEHDZYFdFZwIvQADJgMVWOFcFXBiiZCNVzwRBPO+oCsnXe2IAvtGc9MeANCLCiIgw6Cn22BCEo6bP+84M+z++6FIKI5WiniSalwV/obb5yXJf87Fzhfq18Q4BFgwmo9EuNBkOuzu++M+2QFHZQRnJVnZc9CQNbkydqbPW61VXVnbPgIyCIgKXQ0vpn2q0eXKixj+5/BQ8rBCuZWa8qzIEt9m2jL+1GQxSICAM1SS2E65B1a6uwzSVgQtKdo+04+OS9jx5YKnQHsvHMxAKb66iZMyMuo0eXfDuNWMMX7BjiR4R33lYdKls3EGRObUVOPbwiQlc9bkMU/C9Dk3dMeMIgGjU3QqU8ZqdoiA+0r7tgqE6IHWR5kucDKPfcgqzEga9VhEyVJbpU5peE4LSsgSwUKWhxAFKAAwIGQJ1GugoXwDroEH+0QIIsgjqo5oj7BLtFc0AaQ9c03eUHYArIQWGgjCOTpaiMUaCAQMSO5PkuffGKjzkNPQZaOh9hKAAZMm4RKwMEdkKXlaJEAWVxr1mc6+OBCoJH6n/+xZTwzZk4FQdRjPAhb/NQAWWinLrjQavQQ1MtPum0AACAASURBVGTiScErBLCCs6OPtk7fr79u42VB6/LLLTDRlYHUpZ2OS4/aL2UKFim7/354j98Rq/5ERo2ipk3I/VphBdAwoaUjrpcC07B9+I71nh7/+MeiLLZ4CLq5z96LvXtjUpNA60e8NE18CwBr+vv3vyUAo5hPN9kkBFHPPgswthpKVo3uvXdeJk0Ky5UWixAAc2+/be/ccgsANxd8c/CEpGCIa30PCoYAXWQ3bfuXgiy6KN8Epmj73vhOaM+Y9bvkGpA1ZaoFWZiWoaug7cYbrXk2LlyG21/0vFUmRA+yPMhygZV77kFWY0DWb/abKElyq8wpPzqQFZ3cK10jTBBU+CHdeqt11lZTEm30P3vOWXkIiFJhhmkQgYpg0nTyKQXB/EMivhWZ4JkI0QcftCv1MBm6bRRkoREBZGn4BIQcIAvTE+NUkKV9YVYkgjrt0bypg7yWW9OnHQvtyZomTszLXHMjtO09+tl1V1Yd2mvq8pzQpl9WDQKyHn4YHzGroQFkEU+KOghg6pMIk8DKussuy3X0CcDBiVzrwHMV2Do2HR9l2jf0eAfXXosW0dL/zW9ENtlEnyRZgExoszk2qwzhGUBHk/av1+6R7X8AOq52kU2hV1rJtkcLCNjVBK9YpMCRtOKK8AINlL3mnfIchP8A2L70Eu8g3xGXTOlwfPFF65fFmEmEENltt5y89FK4pw18giYJvvEe1KwHf7UsqCAiSy5ZkM03t2PhHwXqKzAHIJL1PWCGJGAu4wOIUZd7pGqBX7WvuGOrTIgeZHmQ5QIr99yDrAaArIUHyG/2nZgot8qc0rIgC4CAcLrzTkxRdiUepiWECRkQhXaLc0wmf/1rXsYcm5dRo3JyyCHtctzx7TJ2bE7Gjy8GwR232qYgJ51swwRc8b85ueCCdjn+hJzstVe7HHFkuwwd2i53/H2WPP/8LJk2LSfPPZeTF17IyfTpOZk8OS9nn23jFz3wQF4mT6Zuuzz2WE6eegqwlgvy00+zSq4QADcCVFphihN2Tj76KBeYFzExEvoBkMfY0Vocd1xBDj3U5iFD7SpBHKBvmFSQc87Jyw475OWxx+0edpiQMGUhXBHahx2ely23zAWaMYTnYosXggjmCF74hyaN1YQ4UKNRMz3b5X+Wa5ett80FeaGFcrLCijl5+mmc/vPy5FP2SJBWMivtcAAnY0pjdSJHrp96ym6Hg+YQADBsWFHmnbcohx9u81ZbWSBErC8FTHHHE04oyt/+ZgEvWkmei4zJl8yzRjPvBJA0dGg+8JtC69SzV1622DIfAKWXX7Yxyvg2AE68g9dey8lbb+WC1aR/2c6GvMCXCl+3mTMtyCKOFSCL0Ag4luMDGE35vMh889nn5Xng7a67tstJJ7UHMd2I63bttflgE3MLTNFEFuSQQwrBYoYRI+w575zzgw6yz3LSSXYlKt8F7w+gBn38sojJxmpUfAbRxP7vlXaFpAWPll+0I2MuhvedSa0yIXqQ5UGWC6zccw+yGgSy9pkov0mQW2VO+dGALBWuSSd+hBNA64YbWC6fl7fftnvvIXzIxKcCcHCO39bAgXlZf/28rL1OTtb4bU5+t35ONtgwLxtvUpT1BxZkjTWJK4Qjc1H22LNddtixXTbbLCdrrdMum/25Xbbaql1umNQuN944S66/nkjaObn55pzcdFMuAG9EV8dvizxuXLuMn9AuF15IvKW8XHQRRzREOKHbzJYraCvQRrw2E+FuQ0W8+y6r1azzOWO/6SarferVuyB95ipIr945mWuuXLCHHU7t7I2HKfOtt/OBrxD+Qmi30OqRr7kmJ4ceGmqm8Olaex2r1UNIA7AAdpqXX75dTK926dErF+SevXOy7baMPd+RAR74gZFxwr/qKky2aJu4LnRccw/tIACI94tfVr9+dnsetughL7dcCBb0G4gezz4boImjv6WtzwYoxKwal99/Py9LDLArKnv3KUivPnnp0ycvJ5yYD8IwEEeM4LJTp9prgPCDD+YC0zDmYd7jL5bMydQHLFgEMLI6EX7RPxs146NXKRHsla2E+EbJY8e2ByZDFk+Qd9nFmnB5B/h6oV3s2asQ8KRPn4K4mVWhvfsQPsT2x7cPyFQ+AaxmzMjLG2/wHbEqMi8TTmuXffa1q1Ndny++qS23ZFshqxWrNP7o/VaZED3I8iDLBVbuuQdZ9YOsvgsPkNX2npgot8qc0tIgi//SMQM++WQuCDDqbkfimgsfeshGQ0cDQcZMhIZITTIafoH/8kkIMfxcPviAVWE2PASCCqGGuRB/Lba8IdI216xCRAhrwl/n889zAR29V+mIAEbwqQmOevSjCR8mfJnwadKkwlWvqx0xWaoTPfXYixHfpjgNDPcoI55U0sT43fEmbdeZeoC1gw4qyAMPsh+jBYi0r9U3Zlx1bn/1VftsmNpIvGOc+tXEGzeeuOeiPoAUzSgR+xlDXBo9WqRnT/veoMO3Rp+aAI7HHGOvr7rK+klhZoxLrETFbKvBYN060CZ8CSBdTdmYoNHSahBUtz7n0VWr0fK461aZEMtAVg8f/by7oon7frP/7aX5zQOyVt/z9EQ5Df2GA5osEmyW4zvCIk6oxU341ENgoakCBGG6I/iiChnauCDr0kuLcsghlj4r9DD/IIjQBpDwVcLfRoUg2gV8XABL0MeBHBCEQGU1F/0SGgIzDasDASYaXZwxsGnvF18kA1nQBWRp31E+4MOEL5ObonXcsug54wFYKrgAdDBeAEY0Aa4oqwY8om0YSyWgEa2b9prNnlkZiRny0ktZ3WffZa1+AdJonUgAbbSCbuKdsfigUorjM0Fq+dbQRmGmdsGxS+fuuy1weuCBcid3rcMzAZwY4/77E4Gfelpafjz1VFv3xBNnHytjJE4Xfn3qT8a9447PyfDhoQbTpUh5Z1OrTIgeZGVfuHsAlo13lOY333ehAbL6HqcnymnoZxETNXxMWQBZFuywdYj1+cEvis2g3eQKyKOPLsq551rBAqBiFaCaUKhHWABdPcc1IAtwgl8XZjIc5wFB9MumvIAitFn42rDaDCCGsCQBzjBVou3gvFayJrsQqLjjtr49IgccUE7FrVNeMvsVdTV+GKVcE6RTn9dtAdCkjDpJE3VrgZ2ktCrVQ0uIcz+xn+A1YSGS9ItmEoAJj9EG6QbT2g9gm2+oUqKPKC8AZcQ3I6wEvmbQjktffmlB1kkn2VWcqiXVujNnWuDEs6y6qsigQVpSfgTEYcIm77NP6MCutdBUspCDfwjc72306JyMGBE62mt9jtFncssqnbfKhOhBVjYEuAdS2X8PaX7zgKw1dj89UU5Dv+GAJosEmwmyKk3w0fsIdTKaCCt0Q8durasCEkCExkA3F/7gA1b+Wc2RmunQ6uhGy9BFcKIJA2hdeaXdfBp6lAG8aIdTPb5GrNrTVYn0jdAjPERSkIXwhZ4mVwC+8IIV1FdfraXh0a0X3i0/c3ngamzYBobAnNFEqIUpU2a/H60XvYYvScYTbZf0GkDC1jZPPmnDQTDGJH1SB5CFzxIaOnelIX0r4K42Dve5OIcewJvI+7zrKHhyaS2/vMgf/mCDjkZDMnANaLrm2mJgVsS8GJfQUvH9XnGFPcILN7FwAZBFjC9M3JqOPDInRxwxuyZLvwmtl/TYKhOiB1nZF+4egGXjHaX5zQcga7fTZY0EOQ39LGKiho8pCyBLtUovv1wMHMpffz0XmPBUYKgg4ciGvggp9TNCa4UPFmWAG4Qkjsc4NZOgrdoJQNaNN1qzEGUIbQAUvl9oDXCCnjQpJw88EAo3tFsIPEAWQrhWwldHtWDUZVyazjvPgqx33tE74dGtF94tP6MOmT4AB2qS5FkBHfqctELocw/NSmdTEsDTWZpufcbOxs733QfQsto23k0SHqDFY+EDz+YCTeijxdLgpm5/7rnbB/xjLKNH54PVgFzDW7eO23bPPUUWWMCaauPMiuwJue++OL0TPsNtGZ4Tq43vF+0rx7vuKn8/rKjFfMk/BYAsHQsLMdBmRUEg5Von7KX2WatMiB5kZUOAeyCV/feQ5jcPyFpz19MT5TT0Gw5oskiwu0EWAgLhxhEz0qWX2pV5LqBRQcLxxhsLgcZAAYXrh4OfEpl9+8gkhKEKRAT5nXfmOwKZAiYwBaLFQqixZP7ii3NBHCTa0h8ga9YsG46BerWSjkHbQ0PTTjsRuRy6eic86jOGd2Y/0zo8D+AAQEDCpwnQwVETWp3oPS2rdaQfeNOsBB933KkgN9+Mg7fVXhJLzOVVpb5x5r/vfrv6MTpGQJf60lVqrzyknG+IvQhZ0clKR+jBUwWvURqXXgpItgA+2jd1cWT/7Vo2jAXmxbh0/vl2SyLKRo0qCg7zbiL8BCtKGacNSmrLccw/5ZTcbP51SXjm0tfzVpkQPcjKvnD3ACwb7yjNbz4AWbucLmsmyGnoZxETNXxMWQFZTP5Tp7KlTE7eebfcY9gVjOPGsf2JFTwIOoS0rjjjv3y0SGyJM7NkhkGQqtBk9eCUKfnAHAhNtFc4vavvCxsFE6YBbQLJxiSyAAwQAOCqlXC+j24FpG2WXlpkiy30qvzoPmN5SXjlClQcwFVjxjNGtVZot9jIWcFoSKX2Gf3EgYjaLZPVgD/77We3TwI06WKEJK1pS0DUyZNnfxc4+PM9KKiOo+fyGUCFyZIgn6rx414lns2YYUHWhReWAyPth6C0xKxadtn4cvoeObIoF11kyy+7zAZm1ffKd4qpUAEn36U+C+bMM87IBeE8tD+O2ta9l+S8VSZED7KyIcA9kMr+e0jzm++74AD57U6nJ8pp6Dcc0GSRYHeDLIS5gqC//x0BSiDPeJBFPYTz9ddbIYUAQqhq+AKEIyZDAoSi0UAAcU8FUXs7m0Pb7WjQaqFRAWShxSKxpyExsBR0oUnQKNwEBE0CsgA+atKhX+37s8+sqXDChMoiUOtWquGWAyhYGacJnyzX/wqzVJyfltavdqQf3ovbX7X6nS3jfR19dEHOPNOO/7rrCvLoo7ODpji6aDjPOcf6c0XLAbh8D8r/aDnXPJM+F1pHABsrCxWwYmaN+lspHT6Tn/yElZEh37WMI8B3rrmLsumm8eWYOjER8s8ECcd9rjF3k3T8+s0B8vX7JR4akfuJu6bJfRa9l/TYKhOiB1nZF+4egGXjHaX5zfdbcICstePpiXIa+lnERA0fUzNAVmcmf4CTak2Imo1PFOEU3KRCkXhYrAJ75hkraBCmCFUN3QAdQBYCm5VmXLtaCUI0sH0KWh/8r/DFoq3Sv+MO27+OB2GHUKccMIZjfLVEPfpXIa10aXPbbRZkPfZYZQpu/Wgtytxyxo3JUO+pb5PWw3QVt+IwSrfSNTxQ2pXqpL0P2DjttIKMGWPBBfshspVSkgQgAWQRhiOaot9DtFyv9bl4V2PH5uWYY0JaAHc1w2p99zhkSFGWXz4eRLFBOObEXXeNL0fTBahSvzH1y3r8cVsfvvDPAaCfMfKPAN8v4B6QdeNNbBBtV8YyJn3X7viSnrfKhOiCrMF9thc3D11gL+nIK4+SoaU8aL2TxM0bDRormtfZ5jTRvO5WE8TN6282XjS7QS85/92fx3fkDdrGiWatr8doO/d6w8GniubBax0vmt2xcj5k9WM7stbhOGS1MWV56ErHSEd2nj/gwyqjZajm5Y+WoaXctvRIcbPyLDiueLQMdbPb7ldHSJvm5Y6SNid3jIHxOG2GrDpG3FxG2x37SseU03NouPSCc3d8zjO3LXOYVMpl/dJe+cLRoeeOlXP3uZKWuXzh3KXv0uvgZYmn7jsvGx9jdHjljqOM9opHS5rffACydjhN1kqQ09BvOKDJIsEsgCwrUIpy8cV5ufPOnHz7bTzIQlMDyMIJnYTpCJDlCkVWZ7EkH58kBKaaW+gDwEQkecxTmAQBTrSlDFBBtPMHHkCTZSNrI9zQalEGKCNXS7ZeeZ9a//DDRfr0Efn+e70z+7Ga0IyWAeQAWQoi4QPgUTVcnNfyT5p9BOGdZoEs6DJu3sFBB1lwQbR8rqtpoHRkLI4473zrt6T39Ahghw+14oLBSxLayX33JaxH+F4ZH98Ex2ji3phjLZD66qtoqYj6bO219+xtqU04jf33DzW3jAMe6J6IfLMffmi/R33fvF9WUwKy7rnHgiz9p0LrzD6S2ndaZUL0ICsEYx5kRUCgB1llIC7Nbx6Qtfb2pyXKaehnERM1fEzdCbIQEmoqxIR03nl5mTI1F0Red8UE9Uj4whx2WKhhwcSDUFXNEXXwZ5k61QbsREApfWigmcK5HYHOHneALNpShqkHYMKmv6wmRIsAyEKwktF6Ub9aAtChHaFPaOq4abP++iJrrVWtdXXNhEsLKlwDVjB5keAf4ycC/MyZ9pyxpE3QjwMaaelpO4AU42avR7Q68Pjjj+1qQTQ7tRIbiGNehEaUJ7QFWEZXHUZp0o48c2ZBdt45XxZvi/uALAXnblveK0FH0Vbdc49bYs/32svucbjnXvFAj9WH7K3ppjPOKMqxx9rxMHa+Qx0f9fiG33/fgiz2zWSbHUAXiXppU6tMiB5keZBVps1yNE+uFq6SFov7UY1PmabIoedqieYYTdYCA2Tt7U5LlFtlTmkpkIUQVxBErKu//S0vzzwTHwsIYcJGxBdcEApXdXRWGtRBy3X/FBvBHQGlQAHQBMgCQF1+OYEwLchSQISpB5ACCCOsA/5YAAASNDgHtCi9oCDyh/6oo0JSheCsWSLzzCNyyCGRBpFLbRe5HVwqLbcMnyxXa4OvGqanxx6ze0C6dTt7Tn/VnrWz9LS+mjnxmwNksYUMvMZJf9q02qCBvS3RVAKy4Hc01QpISn3l8113sb9lPtB6unQAgi5w1zL6AwSxvc6YMXo3PBJHa8MNrUmQDbXdRFviaN1yS/l9/Ob23pvFG3alJf846Phoz/f52msWZBG6gtWQZFLcN+H2We28VSZED7I8yPIgKzQLu0AwCh7T/Ob7LTBA1tn2tEQ5Df2GA5osEmyWJqvaBK9lCHEV5GikTp9oN8V1hYcKHFYQIpQJvKnlgAza6TV02RbHBVlahtkPkIW/yzXXEHTUgizaUOfhh20UcZzhNayDhmxA0HGuWiodf/SIcFbtkfZLnWeesf5YkyZFW8x+7bbTUu7F3acvhL4mHKpvu60ot95aFDQ+9SbeTVy/9dDlPZKJc8b7BDjQB4se2HC6WuJ5AcKADleL57ZJEpCU+nwH55yTD8yF0WcEEMWZLrlH2SqriAwe7PaK6dG+Yza+Jpo7EendhIaR59X4bVpGqBHuAzr5ltGgMR4dE++AyPiYC/HHQouFNkv9tpROZ4+tMiF6kOVBlgdZzQVZ6259miTJrTKnNByndSfIUi0SAuL55/MyfjzxqMq3DlGBQwR3hBERs/UeAINrNyF8AVl2W5KwDH8sFZw4WRMvSzeghh7xtwBagDFAFn5haFhIjJP2CHmEbKWEmUlXqamQpO6ZZ1oB/OGHlVqG9912elefV6/1SH88L+MjvfSSBSEAEXyX6k3NAFm8M3iESY/3+dBDFmQ9/XQx2Jg77vn1OTCn8WyAa5zEdVWplnPEMV4dy9370XNW7o0YwTdX7v9HPQuqQ6DDPcalZsRhw2bfm1D3Npw6VWTsWBzqy/nPDgU8r34fOh78CtFk/e+VhY5xu++b8yeesCALrR+aQMAW/zBU45XSr3RslQnRgywPsjzIaiLI+smAssUf7kKQ6HmrzCktA7IQEC7IIn7V3/5m/aFcwaACBzPRsGHl/+nr5tBufQJyErEdkOX61aCJUoBEpPHrrrdBSGkL2MJPC5MhGgJAFiZD3aCXcaq/VZwZSfvXQKQ6Zr3/l7+I/PznelX9GG1L7bh73GdcgCyEPwmTKyCEXG0Pv6Bygj/0q5rGBNVrVoH/jBewy9j32itcVQhYZtyAp0qJYLWsmmRcAN443yvaR7WbcfTY9HvHHW0Q0mg59OEpY9TEOffgx5VXWtD80ktaas2HmBH/8x+RSZOsNsv9/s45h7AVIb2wpQQ+WWPGsBjDlkffN98zmixM4YxDY2m5NDp73ioTogdZHmR5kNVckLXelhMkSW6VOeVHA7JUECIM4zJC7vPP0VyxZU1err46LxPPsIFAEcSaP/usIGSchk89NRRS/Eevq7FcAcNehmhHCAMBSEIo4o+FZkpXCj7ySF4uvTQfOMkzNjaFHj+B0BAIaJFp0/PywguYLjHR2G162KqHgJWvvmrrUC+a0SRh/qENWcsHDBABaCVJUQFLG+5VSmiGeAYSQh3fJrIr4Cu1rXWffusFWYACzeqPpTRHjCD2k9UWouEBZMFj/V7QSgG+NF91dWhSBPDwjUDbTWi31Oxm372tw7fw+eeFIJjnJ5+wAjUvO+xISA/bv0uDc+gDCvV9cM49Eu+VrXNOOSX8BgYOlGBjaMrxx0JrhY+ZfsfDhxfkooss0IeWmy+9FN8wu0E19fWb17ZoWQFZ2j+xslgs4NLo7PtulQnRgywPsjzIajLI2mKCrJcgt8qc8qMBWa7pSrUr7vHMM3MyblxOJkxg37i8/H7TvGy0cS4I0YCAQqiQCdlA5t5NN1kh9/nnIv36FeXCC62wUUHI8e23C/LYYzbWFhopVgR++WVePvvMniPACUjKSka0V4zpT38qiDGaWT2WK2W9Fz3aFWasMivPWk/vW2GMQD7rrEA+J/rjPo+eV2qI47ur/WEvPFbANSoBVNJmQl4QKsPN+BQpvRNPLMj48SHIwY9JvxE2UP7974uywQbl+eJL7LMBKgAhCjz0edEmArJ47wpSOBIhnq18NAOwdtop37G4QdvrEY0ltN2sWkww76KLhu+W90s+4ADbmneCCZBvVr/lbbctBKsiGVs0T7qxIJTvvrv9zmmj37y233uf0DwIP9FmuXQI/dCZ1CoTogdZHmR5kNU8kDXfTwbI7zafkCi3ypySeZCloAD/Jswm+NHEZeJRPfFETl58MS9PP5OXLbfMy+mn5+Sxxwry+OMFeeQRmzl/4gm7r6GGK7jlFivUDj54drPYzJlFefZZ67Oi/+nblYHhf/5cI6R0XKuuWpRf/9qagTAFAb5YgYig14zwv+QSYnmF9airmXJCTLBVit7T43XXiXz3XWdEYPK68AQQAd9JaISifj/Jqc1eU99nmiPmLTL+Q5rRJiqtq64qyIEHhtoyfK30nfDOt922KGecaZ3IAWAAF3evP/XvckcNEAJ8AEQUhAGUAOQHHmhjqLE6kfzqqyHAc2lwzhhVA6dH7mmaPj1897znq64i4ruWirz2Gr5UNuMjyN6IaNJUS+ceCTvC9671445oVTUxHrc95/rb0Dq1jq0yIbogy0cWz0Zkcf8esvke0vzm5/uvJToC8GpA3UrHNPQbDmiySLDRju8qQHH0rbRajMjrNk6VdTrGBMd/7K+8Emo5FCC5gk0Fx8iRFmRtvXX56jrKoTV9erjnG/fwAVIthNLQI8FBCRJKsNBaCWFdCcCgnUHYddZsU6vPWuXwCTBR6flqtW9WOe+NcWEirJQAE7z3uPhY0S1noAHIHTEiBJRojABmbuI9ALLYaFn3tKQcfyj8orojwYfoAo3uGIfbZ6tMiB5kZVOge6CVvfeS5jcfgKw/jZf1E+Q09LOIiRo+pmaBLEx7jz4aL9S++caCLLQaJI0VhIYJIcl/6gqycESPpvXWA2QVZd11Zxey+PTgZwMNTYCjSuDn8cctYLv1Vq1d+QiQAUjFAT/oU+b2W5lS40qSgJnG9ZacUhLwB/gAZD30UPiutIfLLy/fPJn7Dz5o66vDu/p4Rd8HgIZtdxQQc6QfgH93JEx5+A9mKbXKhOhBVvaEuQdY2XwnaX7zgKyBfxyfKKeh33BAk0WCzQBZAKNqwSU//TQvb78TLp3XqNcALAQmgIWMoMZp3U0E9px7bguyllqqPBgn9TAV4sgOLRJHQJZeu7Q4Z8Nm/Gk+/TRaMvs14wFIxdHSsqjAn51K4++gzXGDkja+h85TjJox4yjAq0MOsSbWaPmoUUU566xyYMKiBsASqwxJgN44LR5+eS+/EobsIDYV7YhV1dWJbwXNmus319VjiOuvVSZED7KyKdA90Mree0nzm5+v/xIycNNxiXIa+lnERA0fUzNAFgIWHyVW5MWld98lyrYFWQha9m8DlCGQuLbgiiN+VOUg66mnLCj6+c+LMs88FvS4fRBTCP8VBTuANUBWpbTlliK//GWl0vL76gsTp63CJAkA646Epgb/pCylqEN+pbHFhTZQzRNO/G7i+zjgABvGgfu8Y0BW1B/prbcLwn6ICoaJsk60db6rrk66v2Z0jF09jmh/rTIhepCVPWHuAVY230ma33wAsn4/TtyNzCudp6HfcEDTRQTfM8a8bIx5IclDNwNkIfABWXErntBM4Y/Fij8SZhS0DGwHg1BEC4YwRICy7F4DgqqQmDjRgqzhw+0KPtcMQ/tHHy0IS9w1oe3QIKR6T4+6SmynnfRO9SP0AVJxwhogV83/qDrl+krpG7ABoMxKwqSXBHTGBenE5Ms3EQfSTzutKCecED5nnBaPiOhESdfEfoGEAOmOxPjQZCng644xxPWZZG7oovkqrpvEc5gHWdkU6B5oZe+9pPnNA7I2aBuXKCeg32aMmWmMecsYc2TMD/9QY8wMY8xLxpipxpglY+pk4hYT1MJJR9IMkEXEbUBW1CmZyZ54VYCs//s/C7LUwZlAjAgiQJiCGM4BWaqVov0224gsuSQruyzIevbZUHgCqFilRQwhTQAQpaf39PjuuxawnXuu3ql9BDjQTzShqegubQWaNUBWNY1ddLzNvO7MeOK2m2FbIDRPceA4Wsb7iGrxWDk6/QX73TCW/fdnU+nZ31kzeaC0CY6LmTNrKcGEmHQKaUa9xHOYB1nZE+YeYGXznaT5zQOyNhxyaqJcEEdXhQAAIABJREFUg34vY8zbxphfGmPmMsa8aIxZPjJ5bGSM6Vu6N8wYc0OkPDOXiScoRtwMkMUKP0BWnJD84gsLstSh3XVwBmRhHlSNDHUAWa4WYIklRLbfXmTKFAuybr89FGBokgBZLJUnAc4AHnHmPcqvucaCLJbjJ02YsuLADH3H3U9Kt956STVH9faTpD18SKpZAwADqG6+OXyPUW2V2yfx11wtl/al75hvBZ8sVqryHQH4qU+g2e5IOOHDi6ylGhNid09miecwD7KyKdA90Mree0nzm59v/iVkw8GnJso16K9jjLnXmViOMsaQK6XfGGMer1TY3fffNcZMM8Y8b4zZp8JguP8c+Re/+EXD5n9ADRnt0tVXxwuWDz/MywcfhE7vroMzAhJQpQITWu71Bx9YUERgT4AcKwzPOy/s56uvbLyhr7+2IAs6CGHoxKUDDySoKZHS40rj76Gtws8mmtCoxIHKaL1mXRPpPCvO1XHapWrPjSkPkx6Jb8D1u4q2A8wCmtRfi3fsavF4B++9XwhWF/Lu2TSb+t3BGwAkpkL4kbVUY0KsMG102e1ac1jH/DWP6SteoGdPoPt3kr13kuY3P//8S8hGg8YmysYY/jkKcEXp6OKPrY0xlzgzyM7GmHOc6+gpZaOiN7NyvXhpIIuUVHIDqw2skZosBVksycesE5feepvtdCzIcgUmbdVUqJor7qGNUM3W9ddbkPXcc3Yj4J49i3LMMWEvbEeCJkvNdgi5asBntdVENt44bJ/kDIDFuN3EOBGkcWZEt14zz3lmwIbyrpl91aId5ydVrQ2mPPalBDC9/74FRbqCMK6dC8wpx1yoQAZNI+ZiwA3nF11UlJEjKwPtOPqNuseYGEclc3Wj+klDJ82EW20eaXBZ4jnM1WS1/WyYuNkVDu7ea2tvd5q42Q2u6PqfVPsPfuONThE3D177BNE8ZLUxorntV0eIm93o5G1Lj5Sy/PPh0qZ5qRHSpjlab8DB0qZ5kf2krZSHLrS3VMw/2VOGOnnIPDtJR+63iwwp5dnaL7CXDC3ltoX3FTfr/eBYpW8dX/Tojme28yg9ZxxD++8uHXn+3WSom51nrHifOpXoQbsCPeVRx7HvzjJEs8vPeXYqp1GBXvDM7nO6Y3fvc+6M130Hwbn73TvfQ7Remt98ALI2HisbJcg16G8TA7LOrjBv7GSMecoYM3eF8kzdPs4YM7LaiJoBstjW5b77yoEIguD7762pkM2XSa7pxwKq0Omdcu4BGlRIDR8uMu+8Iu3tFmQttlhRdtklIBX8+egjC7IU7ACw9DysZc++/VakVy8pA2nROnHX0ER4uwlwgEBVMOiWddU5PAJkVXrerhoH74xxKNBN0i+mPLRNmPaisbDi2rsmZsrdlYwEICXKPOCG8yOOKMr555e/rziazbgHH7IWhFSfs8aEWG3K6OqyqnOYB1keZFUCRRXve5CV+DcMyHL/mah2XmNOSWouHGSMec0Yg5Iok6mfMWb+0sg4f8IYg0d/xdQMkMUWKI89NrtgY1NonN5ZNUhynZgRzjZkQ9iOe/a+Pa65psgGG1gx8cknRVl99aJstJGKDQlWFbK6EGBGO8xFlYDPgw9ardhdd4Xtk5wBYgBU0NdEH9wDbHVXYjwI9aiWravHA386C/Yw5QGyMO2xbZEb1T1u/O5iCcpdLZ7yAHDDKkPoxgH+OLqNvofDO47vWUw1JsSK80UXFHRqDvMgy4OsimCqmgbJ0Qx1aMVUQ1ahXYcGSzV/qsXi2KqarPmWkI03PCVRrjGn9DbGvGOMWcpxfF8hMp/gh4Vz/LKR+5m6xHMfr33yq8aYY2qNrtEgSwORTp8+u3D55JPyIKSugzMgAf8rFxQpkEFLg/aod2+RI4+0IoutWDbbrCjLLGOvqUt8LOJkkQBagKxK5rOTT7Yg61//su2T/mUsACqXbhzwSkqvkfUw07lbyTSSdlJaCnj03SVth0kP095RR5X72cW1d8N+UK5aPAAmIAttIwDn3nstyGI/xK5OfB9o0+JW2Hb1WOL6qzEh1po2mlneqTnMgywPsjzIcszkjTYXzre4bDLw5EQ5wZyyqTHmjRKQUmxygjFms9KEMsUY81kp/BQhqO5o5kTTZbQbDbIQdKwsnDlzdsFGENJ//MOaChFCroMzQvmHH8KVhFyroAZ4PfAgju5swWNFBr47++1HQFLMihZUEen9uecsyKINIKtS+uMfRX7960qlle/HmQYR6gCv7k4A0Wg4g64eU9x+gknGcMEFRSH2GZqne+6pzku+Cw1gC22uAVc8O0e+LTRI559fCL4RF7gnGUsj6gA2AVlxiyQaQb9eGgkmxC6bg+rpyIMsD7I8yGoyyFr/JNkkQW6VOaWe+Si2baNBFkIOkBXd9FeDkGIyJEW3SEEDBshSYMVRzwE2J59sQdbnn1vx8u67NsAkwOuLLyQw1b38ckEIZElCu0SOS4CyhRYS2X33uNLq9xDgACq0J5oAc91tpmMsgD343x2gQnnhOqHrvSRHTHoALPKbb4a8rdRWt2LScjRGPLuuIuT88MMLMnZsbVpKo5FHxgHI4nvJYmqVCdGDLA+yPMhqIsjqt7gMWu+kRLlV5pRYoFTPzUaDLMAPICtqtsLZ3Q1CGnVwxk8LkKXJBVkIqj/9CdNgKLAQYFdcYYHXtGmEYSjK9OmFILQDNAA+LhBSuhxnzrSmwosvdu8mPwdkAWg0obUgd3eCT4CL7tKe8A7ov5oGsRKP8KECYO27b+X35rbVTcX1WdHi0bdqFImVtv0OBZk0Kfym3PbNPsekGbfjQbP7TUq/VSZED7I8yPIgq8kga90TZVCC3CpzSj14KrZto0HWK69oIFJMf2H+xz9myauvzpIffsjJrFl52WKLdvnpT9tltdXaZfXVc7La6rnguOaaBXHzb39bDK7nmisvgwblZcoUm++6Ky933JEPYmWxN9133xXkySfzQRBKNFgIenVEf+01kaFDJXCSx1F+pZUsyHrllaQiqbweAp2s4IrzNMCinGpjrghKSgbkdnVWbZLyvTNPBEDD/IvGMkkiThqgjBhb+PaNHVuQ445Dc1UIrseMKci22xbk7rvZAYCwDl2b8QMD9GU1tcqE6EGWB1keZDUZZK1zogxKkFtlTokFSvXcbBTIUs0Ty/GvuQYzCUFFw/zWW7PkzTcBWfkgz9uvXeaZp11WWTVXln+zWkE0r7Z6Uchcr7hSXsZPyMt999l86615efgRC7LOPLMg//lPQR57LB8EoVRToZobx4wR6dlTZP31w7zzzvhxpROB0FeApcfuNNG5T4Fmp6vBldufapLcMSU9J8Cou01StXZoKc89tyjjxtl86qkFOf54C7K4d8opBTnxxIK8+VYhMF1jvu7KDKhztZ3VnqU7ylplQvQgy4MsD7KaC7IGr3W8JMmtMqfUg6di2zYaZGEGvO02G1gUoEUC7Lz2Wk4++cSGVsdB3RhrzkkiYBDcmAddHytibJH79BE5/HBMZKwsjN8nbtAgkVVXTdKTr+M5MGdwoFUmRBdk+Sjj2Ysy7t9Jdt5Jmt/8/H0Xl8FrHp8op6EfC0pa7WajQRbaiPvvtyBLNUlsBj1jRk6+/tqCLDQMgKwrrkimSsLsEg3qiO8XUcGXXLIo222H47kFWdG4RPm8yHzziQwbNmcIT/+UngNJONAqE6IHWdkR4h5QZftdpPnN9++7mAxZ47hEOQ39VsNTsc/TSJDF5D5pEoFIrZlQJ/svv7QgK5ezKwsHDy5Ij54F0T0GtV6lIysRMb+4Cb8jNFfrrluU3/0Oh2cLsqIhDF580fpfXXWV29qfew7M2RxolQnRg6xsC3YPvLLzftL85gOQtfqxMiRBTkM/FpS02s1GgixWt7GycNq0fOCPpWLs449ZWZjrCMmw+OIF+dmiybRY0MRUqEvzlSamQ0DW5psX5L+Xwg/Jgiz8g9x0/vkWZL31lnvXn3sOzNkcaJUJ0YOs7AhxD6iy/S7S/OYDkOXsxal7csYd09BvNTwV+zyNAFnq9M4qO0DW66/nA8d3FWNvvpWTDz+0IAuzXs+eBdlwo2QgC0duQFZciITnnivKHnsWpE8fAlFakMUY3MTehossYgOWuvf9uefAnMyBVpkQPcjKtmD3wCs77yfNb77/vIvJkFXHJMpp6MeCkla72UiQ9dlnFmR98EEIsgjX8PrrOfniC+uPde211h9r9JhkIIuwAIAsNFrRNHMm+9xBrygvv2I1W9EVXWy7s/nm0Zb+2nNgzuZAq0yIHmRlR4h7QJXtd5HmNw/IGrryqEQ5Df1Ww1Oxz9MokIXIwjkdZ/Z//SvfYRr8978tyPr2Wwuydt3VgqzpLyQDWTiy45MVlyg76WRLb/JkC7LcGE1Ehyci/Lhxca39Pc+BOZcDrTIhepCVbcHugVd23k+a33z/eReVoSsdkyinoR8LSlrtZiNBFoFIAVnff28d3BFhn32Wl9dn5joCg66wQkH69ksGsGgPcKsU1BFn94susiCLzYWffLIcjN1+uwVZjz465wpT/+SeA3EcaJUJ0YOs7AhxD6iy/S7S/Ob7z7OoDF3x6EQ5Df1Ww1Oxz1MvyFJ/LCbyp58mEGm50/t77+Xl3XdzHea+vn0LsuKKyUAWzu2YCisFuGQrnttutyBr9JjZA1keeaRI794i//d/cWLG3/McmHM50CoToguy1tjtdHHzCiMniuZVh00UzWvteLq4eeAfx4vmQc4+bdEVVW2/PlI68n8fIm1uXvxAadO8SOUAoUP67SIdee4dZIiTB/fZXmJz77/IYCeXtXHuu3U4H9Rz2zD3KAcAbt2yem6btOeRvlz6br+xz1rigdsmOHdpVhtX0nouDbdN9Nytl/bcpVmFhsub2Z4/YTuXRvQ8zW8+AFnLHy1DE+Q09GNBSavdbCTIeuCBotxyS+iPRTDSmTPz8umnFmRNm2YB0a67JQNZ33xjQValPQi5/+yzBekzV1H23LMoL7xQrskaOFDkt7+dcwWpf3LPgUocaJUJ0YOscgDmCtYyQe0K+h5bl4G2snpVhHniepG+3Hbu+DzIckBwz21Tv5MynlYB3Wl+84CstuWOSpTT0G81PBX7PI0AWTqR//3vRbn33tAfiyCkgKxvvrEga9RoC7Kuuy4ZyIoLQqp9cUTT9cYbBVliiaK0tRUFc6Wm9naReecVGT5c7/ij54DngHKgVSZED7I8yHJBXJnJMilgjIJC9zopjWr1EtJzwVLZM1WjHQFnLo3oeZrffACyXA1ulfM09GNBSavdbCTIuv56u3+gTuQ4wAOyWGGIWXGDDQvSs1ch2AJH61Q74vAejeDu1mdT5s8+Y6/Doqy8CqEjQpD17LPWH+uGG9wW/txzwHMADrTKhOhBlgdZZYAkIaCp2MZtz3kNgJOo3KVZhZ4LihLRLdFy21U7T/Ob7z/3z6Rt2cMT5TT0Ww1PxT5PPSDL9cdiVd/ll+eDQKQqxj76KC/vvIOPVjEAWQQgXXyJZFosDUJKCIe4RDkg64cfCjJ0aFEWXdSaFrXuWWdZkPXBB3rHHz0HPAeUA60yIXqQ5UFWGSBJCGgqtnHbe5BlApC1zGHSliC3ypwSC5TqudkokIVz+mWX5eWNN8KVhW++lZd//MOuLPzqq3ywlc6QIclAFsFHcXonGGlcyuUsyALo7b5HUXr1Kt/fcLvtRAYMiGvp73kOeA60yoToQZYHWRUBUxWtUcU2HmSVwYkAZC09UtoS5FaZU8oY0IiLRoGsTz6xIOvjjy2IwkSIqfCrryzIuvxy64914snJQBbb6FQKQoqIxB9LA48ed1wxCEj6/PMhIFtySZFttvHC1HPAcyCOA60yIXqQ5UFWRcDkQVaZM32a33z/uX4mbUuNSJTT0G8Ehsk8jbQgi/AJL7wwS6ZPzwV58uR2OfGkdnnqqZy88mpeHnk0J+edl5MbbmgPVhyiwTKmIM8/XxC2vqmVP/ywKGRNmAcxO2oGYOmqwyuvtCDr6KOLct11IpdcYk2FZ5yhrf3Rc8BzwOVAq0yIHmR5kOVBVuVvwPXRSvOb7z/XIuWhStywJZHzNPQzD5AaMcC0IOuJJ9tl1JhZMub4WXLsCe1y0MHtstMu7XLW2Xk559y87LRLTtZeJycbbZwP8sI/LUi/+QqBdgoNVZL8z3+GIAv/q2gGcJFeftmCLLbXIcK75hdecMWKP/cc8BxQDrTKhOhBVmUBWxF8+BAO5Q7tUROhe51UG1atXkJ6LiAqe3fVaDd7dWGfRaTt58MT5VaZUxqBq8popAVZ110/S04+ZZZ88kkuiOp+9915ueuuvPzznzaPHJmT0aNz8tpreZkxIy8LLliQbbYpBNonNFBJMv5WJHVyVwd61+FehQYrEV97TTryhx9qiT96DngORDnQKhOiC7LKlu+7gs2fi+dNeTDWOZEfaX7z/QFZAw5OlNPQLwMjrXqRFmRNmDBLLrt8VmC+YwK/996CvPSS9bdC47TnngW55ZZCUP7mm1bDdN550ak+2bU6uSer7Wt5DngO1OJAq0yIHmR58DAnAqY0z5zmNx+ArCUOkrYEOQ39VsVVZc+VBmT95z/5wFQ4ZUq7AIC++aYgd91VkA8+sCDr1VcLssceBXn5ZQuyrM+USFrznevkXkt4+HLPAc+B2hxolQnRgywPstIAjjmxTZrffP8+Pw23i9Jtoyoc09AvAyOtepEGZL34ovXHeuONXGD2e+89C7K+/daCrNtvLwSaLMIwYOIbNqwo880nkg+jO9SWAk4NNGPq5O7c9qeeA54DKTnQKhOiB1keZM2JgCnNM6f5zffv/VNpW3T/RDkN/VbFVWXPlQZk3X77LBl97Cxpb88H4GfatLzcd38YmuH00wsyalQhCEAKyFp11aJsskk6aYD/FSBLndzTUfGtPAc8B1wOtMqE6EGWB1lpAMec2CbNbz4AWT8bJm0Jchr6ZWCkVS/SgKzzz58lZ589K5iz0TBNmZKXZ56xIAtQdMABBSEuFudff12UHj2LMnq0O8UnPwdcAbKg5ZPngOdAYzjQKhOiB1keZM2JgCnNM6f5zffvvbC0LbxvopyGfqviqrLn6izIyucLcuxxs+SWWyzI+u67vNxxR17efNOCLIKR4o/1yCMWZN1/v3V6nzw5nXAAxGnQ0XQUfCvPAc+BKAdaZUL0IMuDrDSAY05sk+Y337/XwjJ0ob0T5TT0y8BIq150FmS9/34ucHp//vn2YN7++OO83H47YRssyHr4YQuy/vGPQhB64YQTLMj68svoNJ/sGoCF47tPngOeA43jQKtMiB5keZA1JwKmNM+c5jcfgKwF9pKhCXIa+q2Kq8qeq7Mg6+GHrdP7l1/mghn/1VetJiuXs17tl11WkAMPtFos4lttumlRllsunXBQfyxWMPrkOeA50DgOtMqE6EGWB1lpAMec2CbNbz4AWT/ZU4YmyGnol4GRVr3oLMi66qpZMvZUaypkyn/88bw89FA+0FpxfcwxBTnjDKvVwp9qgQWImZVOOGgQUo4+eQ54DjSOA60yIa6+8txS+GTZIC970wni5lUOnCiaNxxyqmgeuvIocXPbsodLR3YcfIfOv5u4eUi/XaQjz72DDHFyWYRuH/zUBz/N4DeQ5jffv+dCMrT/7olyGvqtiqvKnquzIGvcuFly5ZUWZKFpuueegkyfng9W/xGyAX+sO+6wIOvVVzEVFoO9BNOIBx+ENA3XfBvPgdocaJUJ0YMsr8maE7VSaZ45zW8+AFmRfzbcfzzc8zT0y8BIq150BmR99ZX1x8JkSPr6axsf6/3380FQUiK+A7JmzLAg65JLLMiaMaP2pB9XA18s748Vxxl/z3OgPg60yoToQZYHWWkAx5zYJs1vvn/PBWVI350T5TT0WxVXlT1XZ0DWtGnWH+vdd60/1jvvWJBFBHhWAd58sw1CqqsB99yzKP/1X0UpWMzVaangg5B2mmW+gedAIg60yoToQZYHWXMiYErzzGl+8/17LChD5tkpUU5DvwyMtOpFZ0DWrbfNCsI3EMaBNG1aQaZMtU7ugKzx4wnvYMswJS6/fFHa2tL5U6k/lg9Cmkhm+kqeA53iQKtMiB5keZCVBnDMiW3S/OYDkOX4Hrp+iNHzNPR/rLiqzRgz0xjzljHmyFoP0RmQdc45s+Tcc0On9wceKMjzz1tQ9cMPBdlvv4JceaW9/vJLayo8/vh0IMsHIe2UzPSVPQc6xYGMT4iJ5zAPsjzImhMBU5pnTvObn7/HgjK4z/aJcgL6tX7Xcxtjbihhl6eNMf9dC790R3kvY8zbxphfGmPmMsa8aIxZvtpAkoKsXK4gY46dJbfdZkHW999bU+Hbb1tQhekQf6wnnrDXd99tQdaUKZ2a+zsq+yCkHazwJ54DDedAggmx2rTRzLJOzWEeZHmQlQZwzIlt0vzmCZEyuPdfEuUa9JP8rvc3xlxQmly2KwGuZs41qWivY4y512l5lDGGXDElBVnHHNMupucsMT3bxfQKc49e7dKrd1569M4FeZ55ctK3b0F69ymI6VmQzz+32+LgX9XZDNDyyXPAc6DxHKgxIVacL7qgoFNzmAdZHmTNiYApzTOn+c13Jg5dDfpJftdgF+qRehtj/mmM6VG6zsxha2PMJc5odjbGnONc6+k+JYY8Z4z5j3POdbX8Xo3yam2bXZbVsWV1XLyPrI4tq+NqJZ59oZNBxo5J5jB3/vohI3NSVr5ZP45yGZYFfmRhDMxdyPrOpns68ft6JVKX36mmJL9r2g/QBiWr3MLOdSZOt4kBWWc3cGS8qKymrI4tq+PiPWZ1bFkdl+dZ83/9nZ3DsvKt+HGUfxueHyE/PC+MSfK7fjUGZC0UsjEbZ0lUcvWMNCsfS9wzZHVsWR0XPMzq2LI6Ls+zuF9eY+91dg7Lyrfix1H+HXh+hPzwvLBmwFquTD8KcyF2zHeMMUs5ju8rhO+67rOsfCxxD5LVsWV1XPAwq2PL6rg8z+J+eY2919k5LCvfih9H+Xfg+RHyw/PC+ljVwiYHRBzfJ4UszNbZpsaYN0r2zGMaPDTXxtpg0nWTy+rYsjouGJ7VsWV1XJ5ndf9MExHozByWlW/Fj6P81Xp+hPzwvLC8iPtdn2CM2azEqnmMMTeWQjg8U4qSEHLRn3kOeA54DngOeA54DngOeA54DngOeA54DngOeA54DngOeA54DngOeA54DngOeA54DngOeA54Dvx4OVBPyHsCnrKND9v5DG0wC9KOi5D83xtjXihljSTbyOHVGttAY8w0Y0zeGEOcEDftaox5s5Q5b2SqZ1wFh2d3NHJQJVq1xnaoMWaGMeYlY8xUY8ySzhi6k2fVxtXdPNvPGPNy6b09FtnloZm/TefVNP201nfTzC050vbdzDmo1piqzT31vKx6+m3W76TWmKr9duvhBW3r6bu7+FFtvqiXH759BQ7UE/KebXvYvodJjlWNbOsDvUakesbFBEdws2alJGNjDCsbY66MgKwFS6tAOS5QOufYiFTPuOj/20YMogKNJGPbyBjTt9R+mLO1QnfzrNK4ssCz/g6/cSIlgCCpmb/NUhddckjy3TRrS456+m7WHJRkTJXmnnpeWL39NmNuSTKmar/dZvOjWt/dxY9K80U9vPBta3AgSWyaSjEsolv3uPVqdFuzuJ5xNWuC00EnGZvWvSICsrY3xlyohaVz7jUi1TMu+m/GD1+fqzNjo81vjDGPlxpnhWfRcXGdJZ7Bp7tLPGvmb7PURZccknw37rzTyC056um7WXNQkjHpi4nOPXo/zbHefpvxO+nMmHhmd05JwwO3Tb19Z4Ef7nzhPps/bzAH6gl5z7Y9OznjuTQCKJyiTp/WMy4muO+MMdONMQ8bY9bvdO/VGyQZm1KITnQjjTGjtNAYM9oYw71GpHrGRf+YNonr8pQxZvNGDMih0Zmx0YxvS/mUFZ5Fx8V1FnhGjBm0yB8aY5Yt8byZv81SF11ySPLdNGtLjnr6btYclGRM+mKic4/eT3Ost99m/E46Myae2Z1T0vDAbVNv393Jj7j5wn02f95gDtQT8v7cGJC1VYPGV8+4MF9qSP7VS8LHVZPWO8QkY9M+ohPdYQ54oA4ga4RWrvNYz7joevFS/78s7W24dJ3jcZt3ZmwAd4Ae75GUFZ5Fx8XYssIzxrKDMeZ/A44Z08zfZqmLLjkk+W6atSVHPX03aw5KMiZ9MdG5R++nOdbbbzN+J50ZU9xvNw0ftE29fXc3P3gOd77Q5/LHJnAgidqzkjq+mSaJesYVZdNDxpg1ojfruE4yNiUfneiyYvqKjkvHq8da5Vov6TEpzwYZY14zxiziEM4Cz+LG5QwxOO0unuk4ehpjvildNPO3qf11xTHJd1Npfqp3fI3su1FzUJIx6XM38ntsZL+NGlfSMSX57SrPkh4b2XdX80Of0Z0v9J4/NoEDSbayqBTynm17XMd3QufjjNiIVM+4fuqMA63Mx8YYnKcblZKMTfuK/oAYx7slp3cc3jlv1NjqGRdjUc0Ru5+z+hHn6UalJGPDZwKzl5q8tO/u5lmlcWWBZy6v/uRsl9TM36a+l644JvluKs1P9Y6vnr6bNQclGZM+d3Tu0ftpjvX026zfSZIxVfrtpuGB26aevruTH5XmC/fZ/HkTOFBPyHu270EwEsLh9w0eW9pxYbLEhAAAJIwCwqfRqdbY1jTGfFTyDftXaTw6hj1KYS8IfbG73mzQMe241i2FAoBnhATYs0HjccnUGtsUY8xnFcJIdCfPKo0rCzz7W+nbIlzJg8YYd7/SZv423ffa7PNa300zt+RI23cz56BaY6o299TzrtL228zfSa0xVfrt1sMHbZu27+7kR7X5Qp/LHz0HPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAeMHRN1AAACM0lEQVQ8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8B+ZwDhDdniX85E9LccH0+ok5nDf+8T0HPAfSc8DPLel551t6DngOtCAHjmvgXogtyB7/SJ4DngMpOeDnlpSM8808BzwHWocD0YlQd3vfsLRZ9iRjzBvGmFONMTsaY54pBR/VfQqJVH2zMebZUl6vdVjjn8RzwHOgDg74uaUO5vmmngOeA63BgWoT4dfGmMVK2+ew1dDxpUcebow5s3R+rTHmd6XzX5T2EWwNzvin8BzwHKiHA35uqYd7vq3ngOdAS3Cg2kR4v/OEjxhjVEu1sTHmtlLZ545/F35dgLH5nXb+1HPAc2DO5ICfW+bM9+6f2nPAc8DhQLWJ8E6n3kPGmDVK15gSteyfxph5nXr+1HPAc8BzAA74ucV/B54DngNzPAfqnQgxFx7mcHFV59yfeg54Dsy5HPBzy5z77v2Tew54DpQ4UO9EuLAx5gZjzEvGmBnGmAs8Zz0HPAc8BxqgyfJzi/+MPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8BzwHPAc8B1qaA/8P8iWXlG73xYkAAAAASUVORK5CYII="
    }
   },
   "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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