https://github.com/blab/mumps-wa-phylodynamics
Tip revision: b8358a0d49d70670dbab9eeffa9972c277b3021b authored by Louise Moncla on 17 March 2021, 19:25:20 UTC
adding in callouts for Wisconsin H and A genomes
adding in callouts for Wisconsin H and A genomes
Tip revision: b8358a0
Plot-all-genotypes-divergence-tree.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot all genotypes/global divergence tree\n",
"\n",
"In order to show where the divergent genotype G viruses cluster with other viruses and where the other genotypes land on the tree, I ran an extra nextstrain tree with all genomes we generated and would like to plot. "
]
},
{
"cell_type": "code",
"execution_count": 496,
"metadata": {},
"outputs": [],
"source": [
"import sys, subprocess, glob, os, shutil, re, importlib,json\n",
"from subprocess import call\n",
"import imp\n",
"bt = imp.load_source('baltic', '../baltic/baltic/baltic.py')\n",
"\n",
"\n",
"%matplotlib inline\n",
"import matplotlib as mpl\n",
"from matplotlib import pyplot as plt\n",
"import matplotlib.patheffects as path_effects\n",
"import matplotlib.lines as mlines\n",
"from matplotlib.font_manager import FontProperties\n",
"import matplotlib.colors as clr\n",
"import textwrap as textwrap\n",
"from textwrap import wrap\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"from scipy.special import binom"
]
},
{
"cell_type": "code",
"execution_count": 497,
"metadata": {},
"outputs": [],
"source": [
"mpl.rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n",
"mpl.rc('text', usetex='false') \n",
"mpl.rcParams.update({'font.size': 22})"
]
},
{
"cell_type": "code",
"execution_count": 498,
"metadata": {},
"outputs": [],
"source": [
"def load_tree_json(tree_path):\n",
" \n",
" with open(tree_path, \"r\") as json_file:\n",
" tree_json = json.load(json_file)\n",
" tree_object=tree_json['tree']\n",
" meta=tree_json['meta']\n",
" json_translation={'absoluteTime':lambda k: k.traits['node_attrs']['div'],'name':'name'} ## allows baltic to find correct attributes in JSON, height and name are required at a minimum\n",
"\n",
" tree=bt.loadJSON(tree_object,json_translation)\n",
" \n",
" return(tree)"
]
},
{
"cell_type": "code",
"execution_count": 499,
"metadata": {},
"outputs": [],
"source": [
"# read in metadata dictionary\n",
"def generate_metadata_dictionary(metadata_path):\n",
" metadata = {}\n",
"\n",
" with open(metadata_path, \"r\") as infile: \n",
" for line in infile: \n",
" if \"MuV_genotype\" not in line:\n",
" strain = line.split(\"\\t\")[0].replace(\"?\",\"_\") #iqtree will do this replacement\n",
" division = line.split(\"\\t\")[6]\n",
" region = line.split(\"\\t\")[4]\n",
" country = line.split(\"\\t\")[5]\n",
" date = line.split(\"\\t\")[3]\n",
" if date == \"?\":\n",
" date1 = \"XXXX-XX-XX\"\n",
" else:\n",
" date1 = date\n",
"\n",
" metadata[strain] = {\"division\":division, \"date\":date1, \"country\":country, \"region\":region}\n",
" metadata[\"KM597072.1\"] = {\"division\":\"reference\", \"date\":\"2013-XX-XX\", \"country\":\"reference\", \"region\":\"Africa\"}\n",
" return(metadata)"
]
},
{
"cell_type": "code",
"execution_count": 500,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"using baltic's subtree function, enumerate the subtree descending from the supplied node\"\"\"\n",
"\n",
"def return_subtree(node, tree):\n",
" subtree=tree.subtree(node) ## this function returns a new baltic object that contains a trait-traversed subtree, starting from node k, for as long as the traversal stays within the starting trait value state\n",
" \n",
" if subtree != None:\n",
" subtree.traverse_tree()\n",
" subtree.sortBranches()\n",
" return(subtree)"
]
},
{
"cell_type": "code",
"execution_count": 548,
"metadata": {},
"outputs": [],
"source": [
"def plot_full_genome_divergence_tree_triangle(tree,metadata,colors,division_order,output_name, tips_list1,genotype_G_node):\n",
" fig,ax = plt.subplots(figsize=(10,12),facecolor='w')\n",
"\n",
" divergence = [0,0.01,0.02,0.03,0.04,0.05]\n",
" #[ax.axvline(i,ls='--',lw=2,color='grey',zorder=0, alpha=0.6) for i in divergence]\n",
"\n",
" # # this sets the vertical dashed lines on the tree; plot a dashed line every other year from 1990 to 2020\n",
" branchWidth=2 ## default branch width\n",
" tipSize = 40\n",
"\n",
" # k objects are tips, nodes, branches\n",
" for k in tree.Objects: ## iterate over objects in tree\n",
" x=k.traits['node_attrs']['div']\n",
" y=k.y \n",
"\n",
" if x==None: ## matplotlib won't plot Nones, like root\n",
" x=0.0\n",
" if 'node_attrs' in k.parent.traits:\n",
" xp=k.parent.traits['node_attrs']['div'] ## get x position of current object's parent\n",
" else:\n",
" xp = x\n",
" \n",
" #### clades are classified as leaf objects\n",
" if isinstance(k,bt.leaf) or k.branchType=='leaf': ## if leaf...\n",
" \n",
" # first, are you a clade? \n",
" if isinstance(k, bt.clade):\n",
" if k.numName == \"G_clade1\":\n",
" c=colors['G_clade1']\n",
" s=15\n",
" z=11\n",
" label = \"North American\\nmumps genomes\\nshown Figure 2\"\n",
" \n",
" end_x=k.traits['node_attrs']['div'] ## get beginning of triangle\n",
" start_x=k.lastHeight ## get height of last child in clade\n",
" lower_left=[start_x,y-0.0005*len(tree.Objects)]\n",
" upper_left=[start_x,y+0.0005*len(tree.Objects)]\n",
" upper_right=[end_x,y+k.width/3.0]\n",
" lower_right=[end_x,y-k.width/3.0]\n",
" \n",
" clade=plt.Polygon((lower_left,upper_left,upper_right,lower_right),facecolor=c,edgecolor='k',zorder=12) ## define a triangl\n",
" #clade=plt.Polygon(([x,y-0.001*len(tree.Objects)],[x,y+0.001*len(tree.Objects)],[k.lastHeight,y+k.width/3.0],[k.lastHeight,y-k.width/3.0]),facecolor=c,edgecolor='k',zorder=12) ## define a triangle polygon\n",
" ax.add_patch(clade)\n",
" ax.text(x+0.002, y-3, label, fontsize=18)\n",
" \n",
" \n",
" # no? I guess you are a normal leaf then\n",
" else:\n",
" division = metadata[k.numName]['division'].lower().replace(\" \",\"_\")\n",
" country = metadata[k.numName]['country'].lower().replace(\" \",\"_\")\n",
" region = metadata[k.numName]['region'].lower().replace(\" \",\"_\")\n",
"\n",
" if division == \"washington\":\n",
" region = \"washington\"\n",
" elif \"asia\" in region or \"japan_korea\" in region or \"china\" in region:\n",
" region = \"asia\"\n",
" else:\n",
" region = region\n",
"\n",
" c=colors[region]\n",
" \n",
" if metadata[k.numName]['division'].lower() == \"reference\":\n",
" s=0\n",
" z=0\n",
" elif division == \"washington\": \n",
" s=tipSize ## tip size can be fixed\n",
" z=12\n",
" else:\n",
" s=tipSize\n",
" z=11\n",
" \n",
" # call out the Wisconsin non-genotype G with a label\n",
"# if k.numName in [\"Wisconsin.USA/15.06/FH91/A\",\"Wisconsin.USA/15.06/FH87/H\"]:\n",
"# label = k.numName\n",
"# ax.text(x+0.0005, y-0.5, label, fontsize=11)\n",
" \n",
" ax.scatter(x,y,s=s,facecolor=c,edgecolor='none',zorder=z) ## plot circle for every tip\n",
" ax.scatter(x,y,s=s+0.8*s,facecolor='k',edgecolor='none',zorder=10) ## plot black circle underneath\n",
"\n",
" elif isinstance(k,bt.node) or k.branchType=='node': ## if node...\n",
" c=\"#696969\"\n",
" \n",
" if k == genotype_G_node:\n",
" label = \"Genotype\\n G\"\n",
" ax.plot([x,x],[k.children[-1].y,k.children[0].y],lw=branchWidth,color=c,ls='-',zorder=9) #color=node_colors[node_types[k][\"node_community_status\"]]\n",
" ax.scatter(x,y,s=100,facecolor=\"black\",edgecolor='none',zorder=z) ## plot a circle at the genotype G marker\n",
" ax.text(x-0.008, y-6, label, fontsize=18)\n",
" \n",
" else:\n",
" # this is the vertical line\n",
" ax.plot([x,x],[k.children[-1].y,k.children[0].y],lw=branchWidth,color=c,ls='-',zorder=9) #color=node_colors[node_types[k][\"node_community_status\"]]\n",
" \n",
" # this is the horizonal lines connecting the tips to other parts of the tree\n",
" ax.plot([xp,x],[y,y],lw=branchWidth,color=c,ls='-',zorder=9)\n",
"\n",
" # add in a legend\n",
" han_list = []\n",
"\n",
"\n",
" # bbox to anchor puts a bounding box around where you want the legend to go, prop part is for text size\n",
" #ax.legend(handles = han_list, markerfirst = True, frameon=False, bbox_to_anchor=[0.8, 1], loc=2, prop={'size': 24})\n",
" for key in division_order:\n",
" marker = mlines.Line2D(range(1), range(1), color = colors[key], marker='o', markerfacecolor = colors[key], label = key.replace(\"_\",\" \").title().replace(\"Usa\",\"USA\").replace(\"And\",\"and\"), markersize = 12)\n",
" han_list.append(marker)\n",
"\n",
" # set axis limits, remove border lines \n",
" ax.spines['left'].set_visible(False)\n",
" ax.spines['right'].set_visible(False)\n",
" ax.spines['top'].set_visible(False)\n",
"# ax.spines['bottom'].set_visible(False)\n",
"\n",
" ax.set_xlim(0,0.012)\n",
" ax.set_ylim(-5,tree.ySpan+5)\n",
" ax.tick_params(axis='y',labelsize=0,size=0)\n",
" ax.tick_params(axis='x',labelsize=24,size=5, width=2,color='grey')\n",
" ax.set_yticklabels([])\n",
" ax.set_xticks(divergence)\n",
" ax.set_xlabel(\"\\nDivergence (substitutions per site)\", fontsize=24)\n",
"\n",
" # in order to get the legend to plot without being transparent, over the plot, it needs to be here with frame set to true\n",
" # bbox arguments are: x, y, with 0 being furthest left and bottom\n",
" ax.legend(handles = han_list, markerfirst = True, edgecolor=\"white\", framealpha=1, bbox_to_anchor=[-0.05, 0.02], loc=3,prop={'size': 24}, facecolor='w')\n",
"\n",
" fig.tight_layout()\n",
" plt.gcf().subplots_adjust(right=0.88)\n",
" plt.savefig(output_name)\n",
"\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Run \n",
"\n",
"In Figtree, I manually rooted the tree (midpoint) and ordered the nodes to be in descending order. Then, export as newick and check \"export as displayed\" or something like that. "
]
},
{
"cell_type": "code",
"execution_count": 549,
"metadata": {},
"outputs": [],
"source": [
"# read in the current date \n",
"from datetime import date\n",
"today = date.today()\n",
"current_date = str(today.strftime(\"%Y-%m-%d\"))"
]
},
{
"cell_type": "code",
"execution_count": 550,
"metadata": {},
"outputs": [],
"source": [
"# try instead, clustering into regions and plotting it that way; we could do: west, 2 midwests, 2 souths, northeast\n",
"\n",
"colors = {\"washington\":\"#2664A5\",\n",
" \"north_america\":\"#93B2D2\",\n",
" \"asia\":\"#EEA160\",\n",
" \"china\":\"#5CA7A4\",\n",
" \"oceania\":\"#CF7E86\",\n",
" \"europe\":\"#5CA7A4\",#\"#544370\", \n",
" \"africa\":\"#B2313D\",\n",
" \"G_clade1\":\"#93B2D2\",\n",
" \"G_clade2\":\"#93B2D2\"}\n",
"\n",
"uncertainty_color = \"#B9B9B9\"\n",
"\n",
"\n",
"division_order = [\"washington\",\"north_america\",\"africa\",\"europe\",\"asia\",\"oceania\"]"
]
},
{
"cell_type": "code",
"execution_count": 551,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Tree height: 0.034522\n",
"Tree length: 0.648004\n",
"annotations present\n",
"\n",
"Numbers of objects in tree: 979 (430 nodes and 549 leaves)\n",
"\n"
]
}
],
"source": [
"tree_path = \"../auspice/mumps_global.json\"\n",
"metadata_path = \"../auspice/metadata.tsv\"\n",
"\n",
"tree = load_tree_json(tree_path)\n",
"metadata = generate_metadata_dictionary(metadata_path)"
]
},
{
"cell_type": "code",
"execution_count": 552,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<baltic.node object at 0x7f81fedc9f98>\n",
"clade\n"
]
}
],
"source": [
"all_figure2 = [\"Massachusetts.USA/26.15/1/G\",\"NewJersey.USA/49.14/G\",\"Virginia.USA/51.14/G\",\"Pennsylvania.USA/51.14/G\",\"NewYork/10.14/2/G\",\"NewYork.USA/51.14/G\",\"MountIsa.AUS/10.17/G\",\"BritishColumbia.CAN/34.16/3/G\",\"BritishColumbia.CAN/33.16/3/G\",\"BritishColumbia.CAN/36.16/G\",\"BritishColumbia.CAN/16.16/4/G\",\"BritishColumbia.CAN/28.16/5/G\",\"BritishColumbia.CAN/31.16/2/G\",\"BritishColumbia.CAN/17.16/G\",\"BritishColumbia.CAN/27.16/3/G\",\"BritishColumbia.CAN/29.16/4/G\",\"BritishColumbia.CAN/24.16/3/G\",\"BritishColumbia.CAN/25.16/3/G\",\"BritishColumbia.CAN/26.16/5/G\",\"BritishColumbia.CAN/26.16/1/G\",\"BritishColumbia.CAN/28.16/6/G\",\"BritishColumbia.CAN/29.16/5/G\",\"BritishColumbia.CAN/28.16/1/G\",\"BritishColumbia.CAN/25.16/5/G\",\"BritishColumbia.CAN/21.16/2/G\",\"BritishColumbia.CAN/21.16/6/G\",\"BritishColumbia.CAN/27.16/7/G\",\"BritishColumbia.CAN/29.16/3/G\",\"BritishColumbia.CAN/22.16/1/G\",\"BritishColumbia.CAN/30.16/3/G\",\"BritishColumbia.CAN/21.16/3/G\",\"BritishColumbia.CAN/24.16/4/G\",\"BritishColumbia.CAN/31.16/4/G\",\"BritishColumbia.CAN/29.16/6/G\",\"BritishColumbia.CAN/42.16/G\",\"BritishColumbia.CAN/24.16/7/G\",\"BritishColumbia.CAN/23.16/2/G\",\"BritishColumbia.CAN/24.16/1/G\",\"BritishColumbia.CAN/30.16/2/G\",\"BritishColumbia.CAN/24.16/5/G\",\"BritishColumbia.CAN/30.16/1/G\",\"BritishColumbia.CAN/31.16/1/G\",\"BritishColumbia.CAN/27.16/_/_\",\"BritishColumbia.CAN/13.16/G\",\"BritishColumbia.CAN/33.16/2/G\",\"BritishColumbia.CAN/20.16/1/G\",\"BritishColumbia.CAN/18.16/G\",\"BritishColumbia.CAN/28.16/4/G\",\"BritishColumbia.CAN/27.16/4/G\",\"BritishColumbia.CAN/19.16/3/G\",\"BritishColumbia.CAN/18.16/3/G\",\"BritishColumbia.CAN/19.16/2/G\",\"BritishColumbia.CAN/22.16/2/G\",\"BritishColumbia.CAN/24.16/2/G\",\"BritishColumbia.CAN/18.16/4/G\",\"BritishColumbia.CAN/18.16/2/G\",\"BritishColumbia.CAN/19.16/5/G\",\"BritishColumbia.CAN/23.16/1/G\",\"BritishColumbia.CAN/19.16/1/G\",\"BritishColumbia.CAN/21.16/4/G\",\"Illinois.USA/26.15/2/G\",\"Missouri.USA/29.15/FH114/G\",\"Wisconsin.USA/42.15/FH115/G\",\"Wisconsin.USA/2.16/FH118/G\",\"Massachusetts.USA/12.16/5/G\",\"Michigan.USA/4.16/G\",\"Ohio.USA/19.16/FH119/G\",\"Wisconsin.USA/19.16/FH121/G\",\"Wisconsin.USA/24.16/FH120/G\",\"Massachusetts.USA/21.16/1/G\",\"Louisiana.USA/29.17/G\",\"Texas.USA/27.17/G\",\"Massachusetts.USA/46.16/4/G\",\"Massachusetts.USA/46.16/2/G\",\"Texas.USA/18.17/G\",\"Texas.USA/15.17/G\",\"Massachusetts.USA/45.16/7/G\",\"Massachusetts.USA/45.16/5/G\",\"Ohio.USA/46.17/FH157/G\",\"NorthCarolina.USA/11.17/FH136/G\",\"Alabama.USA/50.17/FH156/G\",\"BritishColumbia.CAN/8.17/3/G\",\"Wisconsin.USA/42.15/FH116/G\",\"Wisconsin.USA/46.15/FH117/G\",\"Wisconsin.USA/51.15/FH163/G\",\"Massachusetts.USA/19.16/6/G\",\"Massachusetts.USA/16.16/12/G\",\"Pennsylvania.USA/19.16/G\",\"NewHampshire.USA/8.16/1/G\",\"Massachusetts.USA/10.16/14/G\",\"Massachusetts.USA/13.16/3/G\",\"Massachusetts.USA/11.16/6/G\",\"Massachusetts.USA/14.16/3/G\",\"Massachusetts.USA/11.16/2/G\",\"Massachusetts.USA/9.16/3/G\",\"Massachusetts.USA/11.16/1/G\",\"Massachusetts.USA/10.16/5/G\",\"Massachusetts.USA/10.16/6/G\",\"Massachusetts.USA/9.16/2/G\",\"Massachusetts.USA/12.16/1/G\",\"Massachusetts.USA/13.16/2/G\",\"Massachusetts.USA/10.16/10/G\",\"Washington.USA/4.17/FH48/G\",\"Washington.USA/12.17/FH76/G\",\"Springdale_745/2016/G\",\"Springdale_754/2016/G\",\"Springdale_730/2016/G\",\"Arkansas.USA/47.16/G\",\"Arkansas.USA/41.16/G\",\"Arkansas.USA/5.17/G\",\"Ohio.USA/2.17/FH128/G\",\"Ohio.USA/7.17/FH130/G\",\"Arkansas.USA/49.16/G\",\"Arkansas.USA/50.16/G\",\"Arkansas.USA/52.16/G\",\"Washington.USA/16.17/FH63/G\",\"Washington.USA/17.17/FH38/G\",\"Washington.USA/16.17/FH36/G\",\"Arkansas.USA/12.17/G\",\"Arkansas.USA/48.16/G\",\"Arkansas.USA/13.17/G\",\"Arkansas.USA/51.16/G\",\"Arkansas.USA/43.16/G\",\"Arkansas.USA/44.16/G\",\"Kansas.USA/8.17/G\",\"Wisconsin.USA/50.16/FH126/G\",\"Washington.USA/18.17/FH147/G\",\"Washington.USA/15.17/FH146/G\",\"Washington.USA/2.17/FH84/G\",\"Washington.USA/11.17/FH143/G\",\"Washington.USA/3.17/FH62/G\",\"Washington.USA/3.17/FH67/G\",\"Washington.USA/12.17/FH144/G\",\"Washington.USA/7.17/FH15/G\",\"Arkansas.USA/46.16/G\",\"BritishColumbia.CAN/18.17/2/G\",\"Washington.USA/50.16/FH3/G\",\"Washington.USA/5.17/FH27/G\",\"Washington.USA/4.17/FH68/G\",\"Washington.USA/51.16/FH41/G\",\"Washington.USA/1.17/FH7/G\",\"Washington.USA/2.17/FH47/G\",\"Arkansas.USA/45.16/G\",\"Washington.USA/6.17/FH12/G\",\"Washington.USA/5.17/FH72/G\",\"Washington.USA/29.17/FH83/G\",\"Washington.USA/19.17/FH80/G\",\"Arkansas.USA/38.16/G\",\"Arkansas.USA/40.16/G\",\"Washington.USA/1.17/FH29/G\",\"Washington.USA/11.17/FH56/G\",\"Washington.USA/3.17/FH24/G\",\"Washington.USA/6.17/FH13/G\",\"Washington.USA/4.17/FH49/G\",\"Washington.USA/49.16/FH45/G\",\"Washington.USA/50.16/FH2/G\",\"Washington.USA/15.17/FH20/G\",\"Washington.USA/20.17/FH73/G\",\"Washington.USA/21.17/FH69/G\",\"Washington.USA/17.17/FH39/G\",\"Washington.USA/20.17/FH148/G\",\"Washington.USA/23.17/FH150/G\",\"Washington.USA/30.17/FH32/G\",\"Washington.USA/23.17/FH149/G\",\"Washington.USA/1.17/FH160/G\",\"Washington.USA/50.16/FH4/G\",\"Washington.USA/7.17/FH106/G\",\"Washington.USA/12.17/FH97/G\",\"Washington.USA/50.16/FH33/G\",\"Washington.USA/2.17/FH66/G\",\"Washington.USA/49.16/FH55/G\",\"Washington.USA/48.16/FH1/G\",\"Washington.USA/49.16/FH158/G\",\"Washington.USA/4.17/FH60/G\",\"Washington.USA/1.17/FH77/G\",\"Washington.USA/10.17/FH43/G\",\"Washington.USA/9.17/FH42/G\",\"Washington.USA/52.16/FH75/G\",\"Washington.USA/1.17/FH5/G\",\"Washington.USA/2.17/FH26/G\",\"Washington.USA/5.17/FH10/G\",\"Washington.USA/51.16/FH46/G\",\"Washington.USA/5.17/FH11/G\",\"Washington.USA/4.17/FH103/G\",\"Washington.USA/20.17/FH82/G\",\"Washington.USA/20.17/FH81/G\",\"Washington.USA/7.17/FH61/G\",\"Washington.USA/2.17/FH21/G\",\"Washington.USA/7.17/FH166/G\",\"Washington.USA/5.17/FH161/G\",\"Washington.USA/5.17/FH30/G\",\"Washington.USA/2.17/FH22/G\",\"Washington.USA/5.17/FH162/G\",\"Washington.USA/12.17/FH34/G\",\"Washington.USA/2.17/FH23/G\",\"Washington.USA/3.17/FH59/G\",\"Washington.USA/4.17/FH70/G\",\"Washington.USA/1.17/FH65/G\",\"Washington.USA/5.17/FH9/G\",\"Washington.USA/3.17/FH25/G\",\"Washington.USA/22.17/FH74/G\",\"Washington.USA/16.17/FH152/G\",\"Washington.USA/10.17/FH52/G\",\"Washington.USA/4.17/FH71/G\",\"Washington.USA/52.16/FH28/G\",\"Washington.USA/2.17/FH54/G\",\"Washington.USA/1.17/FH6/G\",\"Washington.USA/12.17/FH57/G\",\"Washington.USA/2.17/FH58/G\",\"Washington.USA/1.17/FH8/G\",\"Washington.USA/8.17/FH17/G\",\"Washington.USA/9.17/FH31/G\",\"Washington.USA/9.17/FH18/G\",\"Washington.USA/50.16/FH64/G\",\"Washington.USA/6.17/FH14/G\",\"Washington.USA/6.17/FH105/G\",\"Washington.USA/8.17/FH19/G\",\"Washington.USA/12.17/FH35/G\",\"Massachusetts.USA/20.16/2/G\",\"Massachusetts.USA/37.15/1/G\",\"Ohio.USA/2.18/FH102/G\",\"Massachusetts.USA/46.16/5/G\",\"Montana.USA/11.16/G\",\"Massachusetts.USA/52.16/G\",\"Ontario.CAN/09.17/4/G\",\"Ontario.CAN/06.17/1/G\",\"Ontario.CAN/12.17/4/G\",\"Ontario.CAN/09.17/9/G\",\"Ontario.CAN/06.17/3/G\",\"Ontario.CAN/06.17/4/G\",\"BritishColumbia.CAN/8.17/5/G\",\"BritishColumbia.CAN/8.17/2/G\",\"BritishColumbia.CAN/8.17/8/G\",\"Washington.USA/11.17/FH96/G\",\"Ontario.CAN/07.17/1/G\",\"Ontario.CAN/09.17/2/G\",\"Ontario.CAN/08.17/2/G\",\"Ontario.CAN/08.17/3/G\",\"Ontario.CAN/13.17/2/G\",\"BritishColumbia.CAN/11.16/G\",\"Massachusetts.USA/13.16/4/G\",\"Rockhampton.AUS/41.16/G\",\"Washington.USA/16.17/FH79/G\",\"Missouri.USA/15.17/FH135/G\",\"Manitoba.CAN/43.16/11/G\",\"Manitoba.CAN/44.16/15/G\",\"Manitoba.CAN/43.16/5/G\",\"Manitoba.CAN/47.16/4/G\",\"Washington.USA/19.17/FH53/G\",\"Ontario.CAN/12.17/5/G\",\"Ontario.CAN/09.17/3/G\",\"Ontario.CAN/09.17/7/G\",\"Ontario.CAN/04.17/1/G\",\"Ontario.CAN/12.17/3/G\",\"Ontario.CAN/09.17/8/G\",\"Ontario.CAN/06.17/2/G\",\"Ontario.CAN/09.17/6/G\",\"Ontario.CAN/12.17/2/G\",\"Ontario.CAN/12.17/1/G\",\"Washington.USA/17.17/FH40/G\",\"Ontario.CAN/09.17/10/G\",\"Ontario.CAN/13.17/1/G\",\"Ontario.CAN/09.17/1/G\",\"Massachusetts.USA/27.16/1/G\",\"Massachusetts.USA/30.16/1/G\",\"Massachusetts.USA/45.16/2/G\",\"Massachusetts.USA/45.16/4/G\",\"Massachusetts.USA/50.16/1/G\",\"Massachusetts.USA/43.16/1/G\",\"Massachusetts.USA/45.16/3/G\",\"Massachusetts.USA/42.16/1/G\",\"Massachusetts.USA/45.16/1/G\",\"Massachusetts.USA/46.16/7/G\",\"Massachusetts.USA/44.16/1/G\",\"Massachusetts.USA/43.16/2/G\",\"Massachusetts.USA/39.16/1/G\",\"Massachusetts.USA/46.16/1/G\",\"Massachusetts.USA/46.16/6/G\",\"Massachusetts.USA/10.16/3/G\",\"Massachusetts.USA/17.16/7/G\",\"Massachusetts.USA/12.16/3/G\",\"Massachusetts.USA/10.16/13/G\",\"Massachusetts.USA/8.16/1/G\",\"Massachusetts.USA/19.16/10/G\",\"Massachusetts.USA/11.16/3/G\",\"Massachusetts.USA/11.16/5/G\",\"Massachusetts.USA/16.16/13/G\",\"Massachusetts.USA/17.16/1/G\",\"Massachusetts.USA/17.16/10/G\",\"Massachusetts.USA/17.16/11/G\",\"Massachusetts.USA/10.16/2/G\",\"Massachusetts.USA/16.16/1/G\",\"Massachusetts.USA/12.16/4/G\",\"Massachusetts.USA/22.16/2/G\",\"Massachusetts.USA/17.16/8/G\",\"Massachusetts.USA/11.16/4/G\",\"Massachusetts.USA/8.16/3/G\",\"Massachusetts.USA/10.16/7/G\",\"Massachusetts.USA/10.16/8/G\",\"Massachusetts.USA/12.16/2/G\",\"Massachusetts.USA/14.16/1/G\",\"Massachusetts.USA/13.16/1/G\",\"Massachusetts.USA/16.16/4/G\",\"Massachusetts.USA/9.16/4/G\",\"Massachusetts.USA/8.16/5/G\",\"Massachusetts.USA/10.16/4/G\",\"Massachusetts.USA/10.16/9/G\",\"Massachusetts.USA/10.16/1/G\",\"Massachusetts.USA/8.16/2/G\",\"Massachusetts.USA/16.16/9/G\",\"Massachusetts.USA/14.16/2/G\",\"Massachusetts.USA/10.16/12/G\",\"Massachusetts.USA/8.16/6/G\",\"Massachusetts.USA/9.16/1/G\",\"Massachusetts.USA/17.16/12/G\",\"Massachusetts.USA/15.16/2/G\",\"Massachusetts.USA/15.16/1/G\",\"Massachusetts.USA/17.16/2/G\",\"Massachusetts.USA/17.16/6/G\",\"Massachusetts.USA/19.16/1/G\",\"Massachusetts.USA/17.16/3/G\",\"Massachusetts.USA/16.16/2/G\",\"Massachusetts.USA/16.16/5/G\",\"Massachusetts.USA/16.16/16/G\",\"Massachusetts.USA/19.16/7/G\",\"Massachusetts.USA/17.16/13/G\",\"Massachusetts.USA/19.16/8/G\",\"Massachusetts.USA/18.16/2/G\",\"Massachusetts.USA/19.16/9/G\",\"Massachusetts.USA/16.16/6/G\",\"Massachusetts.USA/17.16/4/G\",\"Massachusetts.USA/16.16/14/G\",\"Massachusetts.USA/16.16/7/G\",\"Massachusetts.USA/16.16/15/G\",\"Massachusetts.USA/19.16/3/G\",\"Massachusetts.USA/16.16/19/G\",\"Massachusetts.USA/16.16/3/G\",\"Massachusetts.USA/18.16/3/G\",\"Massachusetts.USA/16.16/18/G\",\"Massachusetts.USA/18.16/1/G\",\"Massachusetts.USA/22.16/1/G\",\"Massachusetts.USA/16.16/10/G\",\"Massachusetts.USA/20.16/1/G\",\"Massachusetts.USA/19.16/5/G\",\"Massachusetts.USA/17.16/9/G\",\"Massachusetts.USA/19.16/2/G\",\"Massachusetts.USA/18.16/4/G\",\"Massachusetts.USA/19.16/4/G\",\"Massachusetts.USA/25.17/4/G\",\"Massachusetts.USA/25.17/3/G\",\"Massachusetts.USA/22.17/3/G\",\"Massachusetts.USA/13.17/2/G\",\"Massachusetts.USA/22.17/6/G\",\"Massachusetts.USA/26.17/G\",\"Massachusetts.USA/24.17/3/G\",\"Massachusetts.USA/22.17/G\",\"Massachusetts.USA/25.17/2/G\",\"Massachusetts.USA/22.17/5/G\",\"Massachusetts.USA/21.17/2/G\",\"Massachusetts.USA/25.17/7/G\",\"Massachusetts.USA/22.17/8/G\",\"Georgia.USA/13.17/G\",\"Massachusetts.USA/24.17/G\",\"Massachusetts.USA/22.17/2/G\",\"Massachusetts.USA/24.17/4/G\",\"Massachusetts.USA/19.17/G\",\"Massachusetts.USA/19.17/3/G\",\"Massachusetts.USA/25.17/G\",\"Massachusetts.USA/24.17/2/G\",\"Massachusetts.USA/21.17/G\",\"Massachusetts.USA/22.17/4/G\",\"Massachusetts.USA/19.17/4/G\",\"Massachusetts.USA/16.16/17/G\",\"Massachusetts.USA/16.16/8/G\",\"Massachusetts.USA/19.16/11/G\",\"Massachusetts.USA/22.16/3/G\",\"NewHampshire.USA/40.16/G\",\"Missouri.USA/32.16/FH88/G\",\"Massachusetts.USA/33.16/1/G\",\"Massachusetts.USA/27.16/2/G\",\"Indiana.USA/48.16/G\",\"Washington.USA/17.17/FH37/G\",\"Missouri.USA/2.17/FH127/G\",\"Missouri.USA/46.16/FH124/G\",\"Washington.USA/5.17/FH50/G\",\"Missouri.USA/46.16/FH123/G\",\"Missouri.USA/7.17/FH129/G\",\"Missouri.USA/50.16/FH125/G\",\"Kansas.USA/13.17/2/G\",\"Indiana.USA/1.17/G\",\"Indiana.USA/49.16/G\",\"Illinois.USA/14.17/G\",\"Missouri.USA/19.17/FH140/G\",\"NorthDakota.USA/15.17/G\",\"NorthDakota.USA/14.17/G\",\"NorthDakota.USA/9.17/G\",\"Massachusetts.USA/14.17/2/G\",\"Kansas.USA/10.17/G\",\"Kansas.USA/8.17/4/G\",\"Kansas.USA/9.17/2/G\",\"Kansas.USA/15.17/G\",\"Washington.USA/6.17/FH51/G\",\"Alabama.USA/11.17/FH90/G\",\"Alabama.USA/19.17/FH92/G\",\"Missouri.USA/24.17/FH165/G\",\"Missouri.USA/33.17/FH155/G\",\"Massachusetts.USA/8.17/G\",\"Massachusetts.USA/14.17/G\",\"Missouri.USA/41.16/FH122/G\",\"Alabama.USA/7.17/FH164/G\",\"Alabama.USA/15.17/FH139/G\",\"Wisconsin.USA/19.17/FH141/G\",\"Missouri.USA/28.17/FH101/G\",\"Wisconsin.USA/15.17/FH89/G\",\"Missouri.USA/11.17/FH133/G\",\"Massachusetts.USA/23.17/G\",\"Illinois.USA/11.17/G\",\"Massachusetts.USA/22.17/9/G\",\"Massachusetts.USA/17.17/G\",\"Massachusetts.USA/25.17/6/G\",\"Massachusetts.USA/25.17/5/G\",\"Washington.USA/19.17/FH44/G\",\"Missouri.USA/15.17/FH138/G\",\"Missouri.USA/15.17/FH134/G\",\"Ohio.USA/11.17/FH132/G\",\"Wisconsin.USA/15.17/FH137/G\",\"Ontario.CAN/37.09/G\",\"BritishColumbia.CAN/14.11/2/G\",\"New_York.USA/53.09/3\",\"New_York.USA/40.09/1\",\"Ontario.CAN/38.09/2/G\",\"Ontario.CAN/40.09/1/G\",\"Ontario.CAN/50.09/1/G\",\"New_York.USA/01.10\",\"New_York.USA/40.09/4\",\"Ontario.CAN/50.09/3/G\",\"California.USA/40.11/G\",\"NewJersey.USA/16.14/2/G\",\"Indiana.USA/5.14/G\",\"Wisconsin.USA/20.14/FH111/G\",\"Wisconsin.USA/16.14/FH110/G\",\"Wisconsin.USA/29.14/FH113/G\",\"Wisconsin.USA/20.14/FH86/G\",\"Wisconsin.USA/16.14/FH85/G\",\"Wisconsin.USA/24.14/FH112/G\",\"NewYork.USA/10.14/3/G\",\"NewYork.USA/10.14/2/G\",\"NewYork.USA/8.14/G\",\"NewYork.USA.USA/51.14/G\",\"Ohio.USA/13.14/10/G\",\"Ohio.USA/13.14/6/G\",\"Ohio.USA/12.14/9/G\",\"Michigan.USA/12.14/G\",\"Ohio.USA/11.14/2/G\",\"Ohio.USA/13.14/7/G\",\"Ohio.USA/16.14/2/G\",\"Ohio.USA/16.14/1/G\", \"Wisconsin.USA/7.07/FH107/G\",\"Wisconsin.USA/11.07/FH108/G\",\"Wisconsin.USA/7.07/FH154/G\",\"Wisconsin.USA/11.07/FH109/G\",\"Ontario.CAN/04.10/G\",\"Ontario.CAN/08.17/1/G\",\"Massachusetts.USA/12.17/G\",\"Massachusetts.USA/5.17/G\",\"Georgia.USA/2.17/G\",\"Massachusetts.USA/18.17/G\",\"Massachusetts.USA/22.17/7/G\",\"Massachusetts.USA/23.17/2/G\",\"Massachusetts.USA/19.17/2/G\", \"Wisconsin.USA/15.06/FH131/G\",\"Iowa.USA/06/G\",\"Wisconsin.USA/28.06/FH159/G\",\"Wisconsin.USA/41.06/FH153/G\",\"Wisconsin.USA/37.06/FH93/G\",\"Wisconsin.USA/41.06/FH99/G\"]\n",
"\n",
"for k in tree.Objects: \n",
" if k.branchType == \"node\":\n",
" if k.leaves == set(all_G):\n",
" genotype_G_node = k\n",
"\n",
" \n",
"for k in tree.Objects: \n",
" if k.branchType == \"node\":\n",
" if k.leaves == set(all_figure2):\n",
" ancestor = k\n",
" print(k)\n",
" tree.collapseSubtree(ancestor,'G_clade1',widthFunction=lambda x:len(x.leaves)/40)\n",
"\n",
"# check to make sure we did collapse a clade\n",
"for k in tree.Objects: \n",
" if isinstance(k, bt.clade):\n",
" print(\"clade\")"
]
},
{
"cell_type": "code",
"execution_count": 553,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x864 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"output_name = \"/Users/lmoncla/Documents/Mumps/paper-and-figure-drafts/eLife-submission-2020-01-08/resubmission-2021-03/figures/individual-PDFs/global-all-genomes-divergence-tree-triangles-\"+current_date+\".pdf\"\n",
"plot_full_genome_divergence_tree_triangle(tree,metadata,colors,division_order,output_name, G_clade1,genotype_G_node)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## pull out the subtrees for the divergent Washington sequences "
]
},
{
"cell_type": "code",
"execution_count": 536,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"using baltic's subtree function, enumerate the subtree descending from the supplied node\"\"\"\n",
"\n",
"def return_subtree(node, tree):\n",
" subtree=tree.subtree(node) ## this function returns a new baltic object that contains a trait-traversed subtree, starting from node k, for as long as the traversal stays within the starting trait value state\n",
" \n",
" if subtree != None:\n",
" subtree.traverse_tree()\n",
" subtree.sortBranches()\n",
" return(subtree)"
]
},
{
"cell_type": "code",
"execution_count": 554,
"metadata": {},
"outputs": [],
"source": [
"def plot_subtree(tree,metadata,colors,division_order,output_name, width, height):\n",
" fig,ax = plt.subplots(figsize=(width, height),facecolor='w')\n",
"\n",
" divergence = [0,0.01,0.02,0.03,0.04,0.05]\n",
" #[ax.axvline(i,ls='--',lw=2,color='grey',zorder=0, alpha=0.6) for i in divergence]\n",
"\n",
" # # this sets the vertical dashed lines on the tree; plot a dashed line every other year from 1990 to 2020\n",
" branchWidth=2 ## default branch width\n",
"\n",
"\n",
" # k objects are tips, nodes, branches\n",
" for k in tree.Objects: ## iterate over objects in tree\n",
" x=k.x ## or use absolute time instead\n",
" y=k.y ## get y position from .drawTree that was run earlier, but could be anything else\n",
"\n",
" xp=k.parent.x ## get x position of current object's parent\n",
" if x==None: ## matplotlib won't plot Nones, like root\n",
" x=0.0\n",
" if xp==None:\n",
" xp=x\n",
"\n",
" if isinstance(k,bt.leaf) or k.branchType=='leaf': ## if leaf...\n",
" #x=decimalDate(k.name.split('_')[-1],variable=True) ## get x position from name\n",
" \n",
" division = metadata[k.numName]['division'].lower().replace(\" \",\"_\")\n",
" country = metadata[k.numName]['country'].lower().replace(\" \",\"_\")\n",
" region = metadata[k.numName]['region'].lower().replace(\" \",\"_\")\n",
" \n",
" if k.numName == \"KM597072.1\":\n",
" label = \"\"\n",
" else:\n",
" label = k.numName\n",
" \n",
" if division == \"washington\":\n",
" region = \"washington\"\n",
" elif \"asia\" in region or \"japan_korea\" in region:\n",
" region = \"asia\"\n",
" else:\n",
" region = region\n",
" \n",
" c=colors[region]\n",
" \n",
" if metadata[k.numName]['division'].lower() == \"reference\":\n",
" s = 0\n",
" elif division == \"washington\": \n",
" s=45 ## tip size can be fixed\n",
" else:\n",
" s=45\n",
"\n",
" ax.scatter(x,y,s=s,facecolor=c,edgecolor='none',zorder=11) ## plot circle for every tip\n",
" ax.scatter(x,y,s=s+0.8*s,facecolor='k',edgecolor='none',zorder=10) ## plot black circle underneath\n",
" ax.text(x + 0.001,y-0.2,label, fontsize=14,zorder=13)\n",
"\n",
" elif isinstance(k,bt.node) or k.branchType=='node': ## if node...\n",
" c=\"#696969\"\n",
" ax.plot([x,x],[k.children[-1].y,k.children[0].y],lw=branchWidth,color=c,ls='-',zorder=9) #color=node_colors[node_types[k][\"node_community_status\"]]\n",
"\n",
" ax.plot([xp,x],[y,y],lw=branchWidth,color=c,ls='-',zorder=9)\n",
"\n",
" # add in a legend\n",
" han_list = []\n",
"\n",
"\n",
" # bbox to anchor puts a bounding box around where you want the legend to go, prop part is for text size\n",
" #ax.legend(handles = han_list, markerfirst = True, frameon=False, bbox_to_anchor=[0.8, 1], loc=2, prop={'size': 24})\n",
"# for key in division_order:\n",
"# marker = mlines.Line2D(range(1), range(1), color = colors[key], marker='o', markerfacecolor = colors[key], label = key.replace(\"_\",\" \").title().replace(\"Usa\",\"USA\").replace(\"And\",\"and\"), markersize = 8)\n",
"# han_list.append(marker)\n",
"\n",
" # set axis limits, remove border lines \n",
" ax.spines['left'].set_visible(False)\n",
" ax.spines['right'].set_visible(False)\n",
" ax.spines['top'].set_visible(False)\n",
" ax.spines['bottom'].set_visible(False)\n",
"\n",
" #ax.set_xlim(0.035,0.06)\n",
" #ax.set_ylim(-10,tree.ySpan+5)\n",
" ax.tick_params(axis='y',labelsize=0,size=0)\n",
" ax.tick_params(axis='x',labelsize=0,size=0)\n",
"# ax.tick_params(axis='x',labelsize=20,size=5, width=2,color='grey')\n",
" ax.set_yticklabels([])\n",
" ax.set_xticklabels([])\n",
"\n",
"# ax.set_xticks(divergence)\n",
"\n",
" # in order to get the legend to plot without being transparent, over the plot, it needs to be here with frame set to true\n",
" # bbox arguments are: x, y, with 0 being furthest left and bottom\n",
"# ax.legend(handles = han_list, markerfirst = True, edgecolor=\"white\", framealpha=1, bbox_to_anchor=[0.04, 0.1], loc=3,prop={'size': 20}, facecolor='w')\n",
"\n",
" plt.tight_layout()\n",
" #plt.gcf().subplots_adjust(right=0.88)\n",
" plt.savefig(output_name)\n",
"\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 538,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<baltic.tree object at 0x7f81fc7915f8> <baltic.tree object at 0x7f81fcc62668> <baltic.tree object at 0x7f81e38e7208> <baltic.tree object at 0x7f81e4763630> <baltic.tree object at 0x7f81fe698b70>\n"
]
}
],
"source": [
"target_leaves1 = [\"Dunedin.NZL/32.17/G\",\"NewPlymouth.NZL/30.17/G\",\"Washington.USA/19.17/FH100/G\",\"Washington.USA/9.17/FH16/G\",\"Washington.USA/14.17/FH145/G\",\"Washington.USA/5.17/FH104/G\",\"Washington.USA/8.17/FH142/G\",\"Washington.USA/11.17/FH95/G\",\"Washington.USA/14.17/FH98/G\"]\n",
"target_leaves2 = [\"Ontario.CAN/13.10/G\",\"31170187\",\"Washington.USA/12.17/FH78/G\",\"BritishColumbia.CAN/28.16/3/G\",\"Washington.USA/28.17/FH151/G\",\"Massachusetts.USA/37.16/1/G\",\"Gabon/9.13/2/G\",\"KM597072.1\",\"Gabon/13/2/G\"]\n",
"target_leaves3 = [\"Washington.USA/9.17/FH94/K\",\"Massachusetts.USA/24.17/5/K\"]\n",
"target_leavesH = [\"Wisconsin.USA/15.06/FH87/H\",\"Virginia.USA/10.12/H\"]\n",
"target_leavesA = [\"Wisconsin.USA/15.06/FH91/A\",\"London.GBR/27.14/A\"]\n",
"\n",
"subtrees = []\n",
"subtree_y_values = []\n",
"\n",
"for k in tree.Objects: \n",
" if k.branchType == \"node\":\n",
" if k.leaves == set(target_leaves1):\n",
" subtree1 = return_subtree(k, tree)\n",
" subtrees.append(subtree1)\n",
" subtree_y_values.append(k.y)\n",
" if k.leaves == set(target_leaves2):\n",
" subtree2 = return_subtree(k,tree)\n",
" subtrees.append(subtree2)\n",
" subtree_y_values.append(k.y)\n",
" \n",
" if k.leaves == set(target_leaves3):\n",
" subtree3 = return_subtree(k,tree)\n",
" subtrees.append(subtree3)\n",
" subtree_y_values.append(k.y)\n",
" \n",
" if k.leaves == set(target_leavesH):\n",
" subtreeH = return_subtree(k,tree)\n",
" subtrees.append(subtreeH)\n",
" subtree_y_values.append(k.y)\n",
" \n",
" if k.leaves == set(target_leavesA):\n",
" subtreeA = return_subtree(k,tree)\n",
" subtrees.append(subtreeA)\n",
" subtree_y_values.append(k.y)\n",
"\n",
" \n",
"print(subtree1,subtree2, subtree3, subtreeH, subtreeA) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 425,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 360x180 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"output_name = \"/Users/lmoncla/Documents/Mumps/paper-and-figure-drafts/eLife-submission-2020-01-08/resubmission-2021-03/figures/individual-PDFs/NZ-cluster-2021-03-05.pdf\"\n",
"width = 5\n",
"height = 2.5\n",
"\n",
"plot_subtree(subtree1,metadata,colors,division_order,output_name, width, height)"
]
},
{
"cell_type": "code",
"execution_count": 426,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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GDBkCZWVlJk1CQoLxnTt16hRqa2sRFBQkUFdPTw9XrlwBAIHbHLKysujUqRPzc01NDUaMGIF169bByMgImzdvhpmZGTQ0NJgyvXr1wqZNm5ifm+u7tLSUdatFSkoKEhISqKqqarFvESI+B9GM8Bvmxo0b6NevH6Y6dcCJySOhICOFsvJqbDidi759+yIjIwOGhoYC9fr27Yu+ffsyP9+9exe7du3CmDFjAABdunQBACgrKwuoO3O5XFhaWgKA0NsfDVm/fj0kJSUxbtw4AMD9+/dZN0kAQEtLC5WVlXjz5g0UFRWb7XvAgAEYM2YMEhMT0atXL6xcuRIcDge9e/dusW8RIj6Hf9wQfo7ry399Vrlq1Sq4Wqrjp2F/zfyU5KQQPLIbHr18h7Vr12L79u3NttGuXTsUFRVBU1MT//vf//62sZWWlmL58uU4fPgwcy1QmFqLvLw8gPqrcoqKis22OWzYMAwcOBBOTk7gcDggIixfvpw1s22qbxEiPgfRjPALUPqr7yfVO3b4II7PGyg0z8fBCH67o1o0hAkJCbh79y6Cg4MxdOjQv+0UdtOmTTA2NsagQYOYNGHqNPzlOP8+bnMsXLgQmZmZOHz4MAwNDZGYmIj58+fDxMSEkfNqqm8RIj4HkSH8RiEivK/+AEVZ4ctTnpxUk9Jcubm5qKioQI8ePWBkZAQjIyMoKyujZ8+eKC4uhqam5meN7cOHD9i5c6eAXJempiaSkpJYaY8fP2aCGLXE9u3bsW7dOubww8rKCjk5Odi/fz9jCJvqW4SIz0FkCL8ACy7XG4GP3SboviMfCblFMNJSEsg7m13EKM40JioqCpGRkSzhherqaoiLiwssMz+FlJQUPHv2DMOHD2el9+/fHz/99BOeP3/OiMAmJCSgX79+rWpXWlpaYKnL5XJZAhdN9S1CxOcg8iP8hgkICMDakzm4WcQOd5ld+BKb4/Iwa9YsofVGjx6Nu3fv4scff0ROTg7Onj2LqVOnwtvbG9LS0p89rri4OFhaWgosd83NzWFjY4MJEyYgJycHO3fuxNatWzFjxoxWtTtq1CgsWrQIJ0+exK1bt7B7927s378fPj4+LfYtQsTnIJoRfsOMHj0aOTk5cAxaBVcbPRhr83DjUSlO/fkAQcGLhSo9A0Dnzp1x8uRJLFiwAFu2bIGqqio8PDz+tuVkQ/3DxkRFRWHixIno2bMnNDQ0sGfPnlaLUYSGhkJJSQmBgYEoLi6GoaEh9u7diwEDBrSqbxEiPpV/THTh7xBM+L8iuvC5z5GXl4c9e/agqKgIurq68PPzg7Gx8d85RBEi/tOIZoT/AkxNTbF27dqvPQwRIv7P8q8whP91f0IRIkT8s4gOS/7l+Pj4MJHi+Jw/fx4cDgeLFy9mpfv7+7Ouv30shYWF4HA4uHPnjtD8iIgI6OjofHL7X4rk5GRwOBwUFhYK5Dk6OmL8+PHMz0VFRfD29oa2tjYUFBTQrVs37N69G8J2lI4fPw4Oh8NI+QtjyJAh+P3335mfS0pKIC4uLnDL5u7duxg4cCDk5eXRpk0bjB8/HuXl5S0+26coBgH1IQyUlZXx4sUL6OvrM0G3Gn+KiooQERHRpCN74/fXEjt27IChoSGUlJQwZMgQoffQTUxMcPXqVQBAcXEx/Pz8oKmpCS6XC0NDQ8ydOxevX7/+6Gdm8U9JX3+LMvtfi3/yXYSHhxOHw6GysjImbcmSJcThcMjBwYFV1sbGhjw9PT+5L77M/e3bt4Xmv337lh49evTJ7TdkwYIFAuP/u+BL/DcMIM/HwcGBCYtQWVlJnTt3ptGjR9OVK1fo2rVrtHHjRpKTk6MlS5YI1B05ciRJSEiQtra20CDvFRUVpKioyPyunj17Rt7e3gLhBj58+EDGxsbk4uJC165doz/++IPatWtH06ZNa/KZqqur6eDBgyQhIcEKN5CSkkLS0tJCP0uXLmW9k+7duxNRfciCgIAAun//vsCntraW9u7dS02Zjobvz8jISGi/RkZGRER06tQpkpeXp2PHjtHVq1dpwIABZGFhwYRCICIqLCwkDQ0Nqquro6KiItLR0aE+ffpQYmIi3bhxgw4fPkzGxsZkbm5OVVVVTb6flvhXLI3/S6SmpiI5ORkSEhJwcXFpUWnG3t6ekbbiR3pLSkqCu7s7YmNjUVlZCS6Xi+rqauTk5DSrP/i5yMvLM1fq/i+QlpaGoqIiZGdnQ1JSEkB9SNOKigqEhYVh0aJFTNny8nKcOnUK/v7+2LBhAy5evCgQ3zopKQlWVlZQVFREcHBwk6f4V65cwe3bt3H+/HmoqqrCwsICwcHBmD59OjZt2iQQ7vVzFIP4NA5ExePxBO6NfyyxsbGoqalhfq6rq8PIkSMxduxYAMCBAwfg5eXF+ITu2LEDHTp0QH5+Pjp37gygPm70oEGDwOFwMHPmTOjo6ODMmTPM76Nz58747rvv0KFDBxw6dOiT756LlsbfCMXFxejRowf69BuE1RGx+CXsd1haWcPV1bXZJZGxsTHU1NSQnp4OoD6m8KVLlzB79mxwuVxcvHgRAHDt2jVUVVWhd+/eeP78OUaMGAEejwcej4dBgwbh9u3bTJu//vorOnbsCHl5eVhbWyM1NZXV5/nz52FmZgYulws7OztmidlwaZycnAx5eXnExMTA2NgYXC4XTk5OrCt4GzduhIaGBng8HubMmYMBAwYgIiKCiW+ckpLC/DG+e/cOEydORJs2baCurg5fX1+8evUKwF9L9piYGFhZWYHL5cLGxob1TJ/C69evUVlZKRCreuLEiQgPD2elnTx5EjU1NQgKCoKamprQ+M0Njc3kyZORlZUl0A5QL25rYWHBCkGqqqqKqqoqlJaWCpTnKwZlZWUJ3Briq/bwPwYGBggJCRFQ7fmY2NGtxcDAgNV3dHQ0VFVVMW/ePACCakP820dVVVUC4yopKWEiEvKNIB9tbW3Gv/ST+eS5ZAuIlsZ/0dK7qKurIysrK5LX706G43aTkd9vZOT3G3Xw3kZcNUMaNWpUs+0PHz6c3N3diah+iSMvL081NTU0dOhQWrhwIRERbd68mZSUlOjDhw80ZswY6tOnD129epUyMzPJycmJnJyciIjo+vXrJCUlRVFRUZSXl0eLFi0iWVlZevnyJbM0bt++PZ06dYoyMjLIwsKCvLy8iIgdIS8pKYnExMTIysqKzp8/TykpKaSqqkpz5swhIqLo6GhSUFCgo0eP0rVr12j06NEkLi5Oe/fupdLSUvL396cePXowS+0xY8aQubk5JScnU2ZmJvXv35/69u1LRH8t2Q0MDOjMmTOUkZFBxsbGTW4DtHZp/OzZM1JTUyNlZWXy9fWl/fv3N7n0d3V1pf79+xMR0dixY0lHR0dgedyhQwe6fv260LE0jsTXkLq6OnJ1dSUtLa0my/BpKjIgn1WrVlHXrl1ZY3vw4AGpqakxaXp6ehQcHNxkG61dGjeksLCQZGVl6erVq0za2rVrSUVFhbKysqiiooL8/PxIV1eXeRfv378nRUVFev36NcXGxhIAKikpae7xPxnRjPAb4Ny5c7iWVwANx6kQk+Qy6RIyStDsMwNHo6Jw//79Juvb29szM8Lk5GTY29tDQkICffr0QXJyMoB6XcPvvvsOYmJisLOzw8aNG9G1a1d069YNnp6euHv3LgDgzp07EBMTg42NDUxMTBAcHIxt27bhw4cPTH9Lly6Fs7MzrK2t4evrK7D04lNXV4etW7eiV69e6N27N0aNGoXr168DANauXYvp06fD09MTFhYW2L17N6NOw+PxoKSkBC6XCx0dHTx48ACHDh1CREQEHBwc0K1bN0RERCApKYnV97JlyzBgwABYW1tj0qRJTF+fipqaGlJTU+Hh4YH4+HiMHTsW7dq1g42NDTIyMphyZWVlOHPmDHMfesiQISgqKsLly5eZMrdu3UJtbS1MTU0/agzPnz+Hm5sbYmJisGbNms96Hr5qzy+//MI67IiNjWWWn3yWLVsmoAS+ZcsWVnvC1MIbrx74LFy4EEOHDmVpaE6fPh16enro2rUr5OTksGvXLqxYsQISEvU7dsnJybC0tISSkhIz++fxeEz9SZMmsfrmbw19CqI9wi9AQk0vAIDxpINC80uuHIS8rhXEJAQFFiQVVCClYoCEhAT4+fkJrW9vb4/AwEA8fvwYSUlJcHFxAQD06dMHP/74IyoqKpCens6c5o0fPx4HDhzApk2bcPfuXVy9epX5D+bk5IRu3brB2NgYjo6O6NevH0aOHAlVVVVmid4wULmcnBwqKiqafPaG//GVlJQYoYgbN24gMDCQyZOVlWX2hRqTl5cHWVlZVltaWlpQU1NDQUEBc9Okqb4a05IARMN8Y2Nj7Ny5E0D9l0R8fDw2bNgAV1dXFBYWQlpaGr///juqqqqYmz4DBw6EuLg4jh49CltbWwD1e12DBw9utt/GnDhxApMmTUJdXR2OHDkCT0/Pj6rfmKZUe06fPg1vb29W2pQpU+Dv789KU1dXZ/0s7Auw4XVIPnfv3sWhQ4cElI8mTJiAiooKnDp1Curq6oiKisL48eNhZGQEa2tr1juTk5MDUP/FoKWlBQBYsmQJfvjhBwD1+pR5eXktvoOmEBnCbwEOB0R1TedTXbO6e5aWllBQUEBKSgquXLnCOF9bWFhAQUEBsbGxKCgoQO/evVFTUwM7OzvIysrCx8cHY8eORW5uLlavXg2g/sDj/PnzyMzMRHx8PCIjI7Fo0SJcvnyZmbE13NdpicYb+3zKysoE8pp6xsrKSoF9IQB4//49ayxN9dUYFRUVAMDLly8FDgRKS0uZ/NDQUOjo6DCCtoaGhjA0NET//v1hZGSEGzduoGvXroiMjARQr/3YkKioKKxduxYcDgexsbHNutU0ZuPGjQgMDMSwYcOwdevWz3J7AppW7amqqkJqaip+/fVXVrqKigpLEVwYwvKF/d/YuXMnbG1tWbPhsrIyHDp0CImJiXB0dARQf7Bz8eJFHDp0CNbW1oiLi2NcjfguYllZWYwh1NTUZPZE37x50+xYW0JkCL8A+eFjms1PTtaC00Bn1NVUspbGAFD95hlqXhay7ts2RlxcHLa2ttiwYQO4XC6srKwA1BsWBwcHhIaGgsvlwtraGrm5ucjJycGzZ88YhZizZ88ybR0+fBgPHjzAvHnzYGNjg/nz56Nz5844d+7c36r4oquri+vXr2Po0KEA6g9DmlpiGxkZ4fXr17h9+zY6duwIoD7Q09u3bxnF64+hQ4cOUFdXR3x8PLp168akFxcX48aNG0yQq4cPHyImJoYxhHy43PrfkaysLF68eIHExEQsWLCANWNLSEjAnDlzkJ6eDlNTU9apfkvcvn0bc+fOxU8//YRffvnlo59PGE2p9iQnJ8PMzOwfFbGIjIzEnDlzWGmSkpKMb2JDuFwuZGRkkJ+fj6qqKsZrwsjICF27dsXGjRvh7OzMqlNcXIxTp07BwsLik8co2iP8BnB0dERPa0sUJ27Gh8p3THrNuxcoTtgEb+8xArONxtjb2yMjIwMODg4QE/vr19qnTx9kZGSgR48ekJKSgoaGBsTFxbFv3z7cvHkTmzZtQnh4OCoqKlBUVAQlJSUsWbIE+/fvx61bt/Dbb7/hwYMHsLa2/luf2dvbG+vWrUNMTAyys7MxduxY1mmhtLQ0nj59ijt37qBLly7o27cvxo0bh4sXLyItLQ0TJ07E8OHDW+3iUVhYiHfv6t8th8PBDz/8gOXLl2Pv3r0oKCjAuXPn4OrqClNTU0Y2bObMmcjOzoavry+uXLmCGzduICoqCsOHD4eDgwOMjY1x9OhRiImJYdasWbC0tGQ+U6dOhby8PI4ePYqzZ8+iZ8+ezPKuJaKjo6GkpAQ/Pz8UFhayPkC983NhYSEjetsamlLt+SdOixuSl5eHhw8fok+fPqx0WVlZDB06FDNmzEBCQgJu3LiB0NBQpKamYvTo0YiNjRXYSti8eTNSUlIwZswYXLhwATdu3MCBAwfQs2dP5gvyk/lHjmBIdGr8sZSUlFDfvn2JIy5JsjoWJKNpQhwxcRozZgy9f/++xfrJyckEgNavX89Kz83NJQC0aNEiJi08PJw0NDRIUVGRxo8fT3l5eaTulgMOAAAgAElEQVStrc2c/oaGhlL79u2Jy+VSx44dKSwsjIiEO1SHh4eTnp4eEQmeGqPRaWhDJ+nq6mr6+eefmXH88MMPZG1tTZGRkUREdPXqVVJTUyMTExMiInry5Am5u7uTjIwMKSkpkbe3N7169apV4yIiAkB79+5lvZtNmzZRp06dSFpamtTU1GjcuHH0/PlzVpn8/Hzy8PAgLS0tkpWVJVNTU1q2bBm9e/eOiOpPSUeOHCn0dzJ27FjS09MjPz8/gd8LH2Hvadq0aQRA6Kfh8yYlJQm019Spsb29vdC/R0NDQ8rKyhJo4+86Nd69ezfJy8uznKT5vH79mmbOnEk6OjokJydHNjY2dPbsWSIi6tevHx0/flygTmZmJjk7O5OCggLJysqSlZUVRUREUGZmJn333XdNjrklvmn1mf8iWVlZSEpKgoSEBJydnWFgYPC1h/SPcPPmTcjJyUFXVxdA/Qmzuro6Tp48yRwwiBDxpRDtEX5jdO3atckwnf+X2LVrF06ePIm9e/eCy+Vi69atUFJSEmkNivgqfNIe4bRp01r8iPg2aHxpXkpKCj169MCff/7ZZB2+KEFjIYCW8hrSkkDDkiVL0KtXLzg7O6NHjx7Yv38/ioqKYGtri6ioqFY/X0hICHr16tXq8q2lpef8XIEJIkJYWBgsLS0hIyMDHo8HZ2fnJg+MAgICwOFwcPz4cYE8fX19qKioCNw6uXPnjlBxCb7wQ3l5OSZPnszElHFyckJBQQFTrqioCC4uLlBSUoKBgQE2b97cqmdrSvjhyJEj6NSpE7hcLiwsLJCWlsbKbyj8cPnyZVhbW0NGRgYGBgYIDQ0V2l5DceKDBw+ie/fukJWVhbKyMvr374+4uLhWjVl0WPIf4MCBA7h//z7u37+PtLQ0SEtLw83NjXUPtCE9e/bE/fv3GcdWHR0dRERECM37VOTk5ODs7Izy8nIEBAQgPT0dly9fhpubG7y8vHDo0KHPav+fxtPTk+Uw/bEEBARg3rx5mDhxIq5evYq4uDjweDzY29uzjBFQv21w9OhRSEhICL26B9S7AgUHB7fY7/v373HhwgX069cPgYGBSEpKwrFjx5CWlgYigoeHB+rq6kBEGD58OOTk5HD+/HkEBwdjzpw5LOWcxtTU1ODQoUM4c+aMQF5qaiomTJiAn3/+Genp6bCwsICXlxfrgCwtLQ0dO3aElJQUhg4dCmtra2RmZmLx4sUICgoScPFpeKCyePFiTJo0CaNGjcKff/6JuLg4dOnSBc7Ozox7U7N88u5iC4gOS74NADAb0HzS0tIIAP35558C5aurqwXStLW1BQ4aWqIlpZqysjLi8XisQxw+CxcuJFVVVaEb7I0JDg7+rE3ypmjN1bdPJTU1lQDQuXPnWOl1dXVkb29PHh4erPTExETicDgUEBBA8vLyAodnenp6ZG9vTxISEpSXl8ek3759W+Aq4enTp8nR0ZFqampITk6OoqKimLw7d+4QAEpLS6Nbt24RAHr8+DGT7+Pj0+TB0K1bt0hKSoo51Gl8YDNo0CD6+eefmZ/Ly8tJR0eHMjMzmbS5c+dSSEgIRUZGUtu2bam2tpbJ8/X1ZfVdV1dH6urqVFhYSFlZWSQmJkYxMTEC45o+fTrp6+sLHXNDRDPCfxF37tzBvHnz4OzsjLFjx+KPP/4QqovXElJS9TdY+P5w+vr6WLNmDezs7ODi4sJaFurr6+Px48fw9fVFSEiIwJLxUwUajh8/jvLycsyePVtgfDNnzkRoaChzM+TKlSvo2bMnZGRk0KlTJ+amR2OELVcdHR2xcOFCAPU3akaPHo2pU6dCQUEBhoaGSEtLg7+/PxQVFaGpqSkwEz1w4AD09PSgrKwMLy8v5qpX477i4uLQtWtXyMjIQEdHBz/++CPrWmJD9uzZAxsbGwG/Qg6Hg61bt8LLy4uVHhkZCRsbG/j5+eHdu3f4448/BNocO3YsrK2tmwzoxYfvLvPy5UvweDyWUAFf5KG4uJhZZjcWRWg4g2tIc8IPNTU1SElJYandyMrK4tGjRyw/Tv7YamtrMWzYMJaDvISEBMtd6M8//0Tbtm2hp6eH3bt3w9LSkrlR1ZCFCxdi9erVTa5+GFo0lZ+IaEb497Jz506SlJQiS7v+NNxvHvV19yF5xTbk7OzcrA4bGs0IX716RcOHDycbGxvWJXsej0cRERH06NEj1mzo0aNHpKGhQatXr6bS0lJW3ucINMyaNYtMTU1bfO6SkhJSVFSkWbNmUXZ2Nh07dozatGlD+/btIyL2jLCh+w4fBwcHWrBgARERjRs3jiQlJennn3+m7Oxs6t+/P0lISND3339POTk5NG/ePFJSUqKamhrmObt06UKJiYl0/vx56ty5MyNu0bCvsrIykpGRodDQULp58yadOHGCFBQUaP/+/UKfydLSkqZPn97isxMR1dTUkIqKCi1fvpyIiHR1dcnb25tVhu8yk5GRQWJiYhQdHU1EwmeEwoQf+KxZs4Y4HA7l5+dTeXk5qaurk7+/P71//54yMzNJSUmJcaVqjsYuPPzZ5cGDB6lLly6krKxM/fv3p+zsbKZMY+GHhmRkZJCysjKrzcWLFzMCHt27d2/1+2wKkSH8F5CVlUWSUlLkv2QnbT6Zw3xWHkglbX0j+umnn5qsC4AkJSUZUUwAJCYmRkeOHGHK8IU4+TReFjZcGjfMO3HiBHG5XHrw4AEREdXW1lJERAQ9f/6cMYQHDhxg2t2wYQN17tyZiIj8/PxataRdsmQJmZmZsdJWrFhBFhYWRPTxhpDfPxFRWFgYiYuLU3l5ORH9ZTgePnzIPGd6ejpTPj4+ngDQixcvWH09efKEQkJCWH/EPXr0oJCQEKHPZGhoyIypJfiqK/wl7+TJk0lRUZEqKyuZMg0Nz8SJE8nAwICqqqoEDOHNmzdJV1dXoI/q6mpasGABcTgcmjx5MpMeFRVFAIjD4TBfCq0RP21sCC9dusSoAx07dowuXrxIo0aNIjU1NXrx4gUREW3fvp18fHxY7VRWVpKEhATTN99vlKj+/SYmJhKR4PssLCwUEIPll20K0dL4X8DWrVvRrfcQGFv2ZKXLKfLgPuEHhIeHN7lkAYCwsDBcu3YN165dw59//omFCxfCy8sL0dHRTJmPVUUB2AINgwcPxoYNG9C3b1+Wjl5TAg1KSkosbcKGEBFev36N2tpa5Obmwt7enpVvaWkpcKDQWhqGNZCSkoKamhqz/OMfAPHfpbi4OMuVie/a0/gUVlNTEyNGjMCSJUswevRoWFlZITMzs8lti+aevaamhiU7HxkZCUNDQ2bczs7OePPmjdADCQBYsWIFXr58ifXr1wvkCRN+uHHjBqytrbFy5UosWLCA8fvNycnB2LFjMX/+fGRkZDB6i6NHjxbab3PU1dXfo1+9ejWGDx8OW1tb7N27F9XV1cyprrCxSUlJ4dq1a/j9999RWVnJiK6+ePECt27dYrwF5OTkWJqRWlpazP/3jIwMVFVVNblNweeL+hFuicn9kt39n+FM0gU4DRsvNM/IvAdelZaisLCwyRCf7dq1Y12Qt7KyQlpaGg4cOAA3NzcATQseNMfnCDRYWFhg8+bNeP36NUtaCaiXDOvZsydzjYy/p8mnsdhCczS+hsbfF+XT8DqiMBrm8/eZGvedlpYGJycn+Pn5wc3NDV27dm1SKQiof/am3GTWrVuHHTt24P79+6iqqkJ0dDTKysoEfj9Hjx4VGtdaVVUVISEhWLRokYBCdmPhhwsXLmDw4MFo3749rly5wtqvi4iIQPfu3bF8+XIAQLdu3SAnJwcnJye8efOG+f22Bv4XY8P/gzIyMujQoQOePn0qIPxw5coVyMrKwszMDKampjA1NUV1dTVjCOPi4tC3b19GiMPExARZWVlM25KSkkxf+fn5rRqjaEb4L0CaK4t3bwSViQGg4l0ZqK7uoyXyxcTEPsn4NeTw4cNYvXo1bGxssGDBAqSnp0NXVxfnzp1rsa6LiwukpKSE+qbt2LEDXbt2hZ6eHoyMjBitRT7nz58XGsJASkqKJQlWV1fXpB9ja/jw4QMTNAgALl++DDk5ObRv355V7siRI3BycmIOOoyMjPDo0aMm2/Xy8kJ6ejrOnz/PSq+ursbevXsxbNgwAPWGq6ysDMePH0dWVhbzGT16NGJiYoRK8wP1On+6urqYP38+k/bu3TuW8ENdXR18fHxgbW0tYASB+rvewgQRxMXFBb6YWsLAwAAqKios4//+/Xvcu3cPnTp1EhB+2LZtm4BIQ3V1NWNQG9+PHj16NDIzM3HhwgWBvps6WGvMF50R3ojbDkB07e5j4Tz0xS9rNqLXoBEQl2DLUaXFHcF3330HbW3tJus/e/aMWc69ffsWx48fR2JiIk6fPt2q/qWlpXHjxg3mxJQPX6BBU1MTNjY2yMzMbLVAQ5s2bbBu3TpMmzYNHz58wPDhw0FEiIiIQGRkJFJSUgAAU6dOxaZNm7Bo0SKMGDECWVlZ2L59u1B/OhMTE5SWluLs2bPo168fQkNDhUrbtxYOh4MZM2Zg48aNeP/+PWbPno2pU6cKzCp1dHQQFRWFtLQ0SElJYe3atSgtLcWTJ0/w7t07cLlcFBUVQUNDA1wuF/369YO3tzc8PT2xcuVK2NnZ4cWLF1i6dCmqq6sZf8DIyEhYWVkxgq98pkyZgsjISMTHxws9KZWQkMCmTZtYp9KNhR+uXr2Ke/fuYceOHXj69CmrvoaGBoYPH461a9di6dKlGDZsGF68eIE5c+Zg5MiR4HK5qK2tZT1Tc4iJiSEwMBBz586FvLw82rVrh9DQUGhpaWHw4MGYPXs2y7B5e3tjyJAhWLNmDQYPHozbt29j/vz5mDBhAj58+ICzZ8+yRGqHDh0KFxcXeHh44JdffmHe56+//or4+HgoKys3Oz4AX/bUWHSA8mm8ffuWOnfuTGY9HCko7BRtPplDaw5fpmETfiApqeY3gtHo0r6kpCR16dKFfvvtN6ZM483txoclQUFBJC0tTatWrRLI+1SBBj4nTpygHj16kLS0NCkqKtLgwYMpIyODVSYmJoZMTExIUlKSOnToQNu2bWPyGvsRBgcHE4/HIzU1NZoyZQq5urqyDksanrg2PlxpOOakpCSSlpamPXv2kKamJsnLy9OECRMYH76GdcvLy8nT05Pk5OSoQ4cOtH37dtq9ezdJS0tTXFycUJGE2tpaWrVqFXXs2JEkJSVJXV2dJk6cSMXFxUybsrKyrGflU1dXR3p6eszhQlNCC56ensxhSWPhhyNHjjQp7MAf5x9//EF2dnYkLy9P2traNH36dHrz5g3rXbVW+KGuro6WLVtG6urqxOVyaeDAgXT37l0iEi78sGfPHjIxMSEZGRkyNDSk5cuXU01NDaWlpTEHZQ2prq6mpUuXkqGhIfM+fXx8qKioiFxcXAR8aRvzRUUXREIMn86zZ8/g7++PEydOQE5RGRXv3qCTcUesWbOmWa1CESJEtIxIdOFfgrq6Oo4ePYri4mLk5+dDWVm5xVCfIkSIaB0iQ/gvo6E8uQgRIv4eRKfG/0I4HA7ExMRQXFwskLdmzRpwOBwmUNO3RkuqNP8WOBwOI0TRkJCQEJZq9vv377FgwQIYGhpCVlYWRkZGCAoKwtu3bwXqvnjxApKSks1KzoeGhmLmzJmora3F7NmzoaKiAh6Ph4EDB+LmzZuf/Vxr1qzB999/L5B+7do12NvbQ1ZWFrq6uggLCxMow1e14VNSUgJxcXGhCj7Pnj1D27ZtGf++jIwMODs7g8fjQV5eHlZWVti8eTPjg/hPIzKE/1LExMRw8uRJgfTo6OhWBzH6r/H999+zviB69eqFkJCQf7RPf39/xMXFYfv27cjMzMQvv/yC3377TeAkGABzEp6Tk9OkwzhfcWX16tU4dOgQ9u/fj5SUFIiJicHNzQ21tbXYv3+/0FCbXC4X+/fvF9ouESEjIwMbN24UyHv16hUGDhwIW1tbZGRkICAgANOmTUNu7l9+wQ1VbYD6aHOBgYFNGrK4uDj069cP4uLiOHv2LOzt7aGnp4eEhARcvHgR48ePx8KFC7+YpJ9oafwvxc7ODtHR0Zg8eTKT9uLFC1y6dAl2dnZfcWQi+FRVVeHXX39FWloaevToAaDexUdHRwe2trZ48OAB9PT0mPKRkZHw8fHBsWPHcPToUSxYsIDV3ps3b5CVlYU+ffpg9uzZ+PHHH5nbGGFhYdDT08Pt27fh6uoKGxsbVt2EhASsWrVKIJQnn/bt2+PBgwdC88LDw1magKampkhKSmL8/wAgKSkJVlZWUFRURHBwsEC0vMbExsbC2dkZVVVVGDduHPz9/VkuMebm5tDS0sLIkSPx448/Cvhu/t2IZoRfmZs3b+J///sfdHR0oKmpCW9vb2RmZrZYz93dHYmJiawlVkxMDMzMzBj5ez5r1qxB+/btISMjAxMTE5au2+3bt9G/f38oKCigffv2CAoKYr7Fm8urqKiAn58fVFVVoaCgwAoyDwBPnjyBm5sbZGVloaWlhUWLFrHG1JQqTUsKMs+ePYOHhwd4PB50dHQwffp01u2RgwcPwsjICFwuF927d0dSUhKAeuWZ3377Dfv27YOjoyMcHR1x4cIFLF68mJkltqSk87G8e/cOtbW1ePjwISvdxsYGcXFxrEBKT548QVpaGjw9PdGvXz+hfpLx8fHo1asXuFwutLS0GOMK/HU9sLKyEkpKSujUqRPzUVNTw/Lly7Fv3z7W9ceGnDp1CllZWUxUwYacPXuWpRzDLz9jxgzm54ZOzpMnT0ZWVhbCw8OF9lVbW4tz585h0KBBOHXqFEpKSljO33zc3d1x9OhRSEtLC23nb6VZ55rPQORH2DJJSUkkKytL3fsMpWkh22nGsl1kP3gUSUlJs0QRGoP/ryjTvn17Vjk3NzcKDg4mb29vJoBOfHw8KSgo0IkTJ+jmzZsUGhpKHA6HEUro0aMHTZgwgW7evEmnT58mVVVV2rFjR4t58+fPJzMzM7p48SLl5OSQl5cXGRgYMGOxtbUlV1dXys7OppiYGOLxeLR3794WVWlaEk4YNWoUDRkyhHJzcyk5OZk6duzIiE4kJiaSmpoaHT16lHJycig4OJgRhSgpKSF3d3fy8PCg4uJiKi4uJmtrawoICKCSkpJmlXSa+h0I02gMDg5m+Uo6OjqSlJQUubq60ubNmyknJ0eowsq6detIQUGBKisraffu3QSACgoKWGV8fX1py5YtAnUrKipo8uTJ1L59e6F6klOnTmVUc1qisa8lEZGGhgaFhoaSk5MTKSkpkaWlJR07doxVRpiqTVOajikpKWRtbU1ERPPmzWuVCtE/jWhG+JX48OEDxo4di0Fe0zE2cDk6W30HI/PuGDl1AbwDlmLChAlCN9Qb4ubmhhMnTgCo36M5e/Ysc3eYj5ycHDZv3gw3Nzd06tQJ/v7+AID79+8DqL+LaWxsjE6dOmHIkCGIiopi3HKayzMxMcGWLVtga2sLMzMz+Pj44O7duwDA3D/eu3cvzM3N4eLigsWLF+Px48fMuJYuXQpnZ2dYW1vD19e3ybu3jcnPz4e+vj66dOkCBwcHREZGom/fvgCA5cuXM/GFzczMEBISgm7duuHQoUNQUVGBnJwc5OXloaGhAQ0NDUhLS4PH40FFRQV37tyBmJgYbGxsYGJiguDgYGzbtq3Fy/otcfz4ccybNw/37t3DjBkzYG5uDj09PQG15cjISAwaNAjS0tIYMmQIOBwOK2QBEeGPP/4QCL25cOFCyMnJISwsDD///DNz/5ZPQUEBdu3ahWXLln3yM5SWlmLp0qVwdXXFuXPn4OHhgREjRjAz5lu3bqG2trbVwh0NZ4+vXr0SuGtubGzM2tdcvHjxJ4+9tYgM4VciLi4Obyuq0dvFSyDPyn4QeGo6LcrVu7u7IzY2FjU1NYiPj0fbtm0FAj/Z2dlBW1sbc+fOhZubG8zMzED18msAgAULFmD+/PkwNzfHDz/8ACJiosg1lzdy5Eg8ePAA06ZNw8CBA+Hj48P0mZeXh06dOrGuNs2cOZO159WUKk1LzJs3D3v27EHHjh0xbdo0PH36lLlKlp2djTlz5rD+iC5dusQY6OZojZJOQ5q7ViYjI8P8m8fjYenSpcjNzcXz588RGRmJjh07YsKECcw95vv37yM9PZ35EtPQ0EDXrl1Zy+OrV69CUVFRYK8sICAAly9fRmBgIKZMmSKwrbJixQq4u7t/kroQn7q6Ovj6+mLmzJmwtrbGwoUL0a9fP8aYC1OOaY6G5RsrxwD1hpKvHtO5c+eWRVX/BkSG8AuxJSaX9Yk4mQYt/Y4QFxd+XqVj0Bn37t1rts1evXpBXFwcKSkpiI6OFpgNAsCqVaswYsQIKCgoYMKECUhOTmblz5kzBw8fPkRgYCAeP36MgQMHMgaruTxXV1esWLECxsbGmDt3Luty+7t37wRmJo1prXoMwFaQ8fLywuPHj7FkyRJUVlZi9OjRGDt2LIB6dZj169czf0TXrl1DXl5eixv3wF9KOqmpqejVqxciIyNhbGyMnJwcoeVVVFTw8uVLgfTS0lKoqKgAqN+zbbg3qqqqilGjRiE+Ph7a2trMfWp+TI3vv/+eCbJ19epVZGVlMUa84SyqtLQUp06dQlVVFVRVVdG9e3esW7cORkZGOHv2LGsskZGRrAO1T0FVVZWlHAMAXbp0Ye4of0yQ+EePHuHp06eMpJmJiQkePHjAuhNuYGDA7G8+e/bss8beWkSG8Cuh2EYFJcUPm3QvKHn8oEXHaXFxcbi4uOD333/HqVOnhBrC3377DT///DOCgoLg5ubG0i18/vw5PD09wePx4Ovri8jISAQFBSEmJqbZvFevXuGPP/5AREQEAgIC0K9fP5ZRMDY2RkFBAcrLy5m0GTNmIDAwsMX30pyCzIcPHzBy5Ei8efMGXl5e2LNnD3bs2IGYmBgA9TJPJSUlzB+RsbExgoKCWnX49LFKOra2toiPj2el1dXVISkpiTm1r6qqwoYNG1j6gkD9701SUpL5MoiMjMTAgQNZCjOJiYms5XFDY/P69WsMHTqUpYxDRKipqYGamhqTdvz4cSgoKMDR0bHF52+OHj16CGxdXL9+HZ06dRJQtWmJ2NhYDBw4kJE3c3d3B4fDwfbt2wXKxsTECPWV/ScQuc98IRor71T274joXSuQlXYG3XqzlxW3r2fi0Z3rGDNmTIvturu7Y9SoUZCRkYGDg4NAvo6ODuLi4jB48GA8efIEISEhkJKSwt27d2Fra4vLly9j5syZmDFjBioqKnDixAnY2NhAWVm5yTwFBQUoKiri0KFDUFRUxJUrVxjduvz8fAwePBgaGhqYPHky5s6di+zsbOzcuROxsbEtPk9zCjLi4uJ4+PAhZs6ciaCgIIiJieG3335jXEXmzJkDPz8/mJiYMLFNkpOTmXcuLS2Ne/fu4dGjR2jXrh2kpaVx+/ZtPHv2rEUlnadPn0JCQoKZ7QUGBsLe3h4//fQTfHx8UF5ejo0bN+LevXuYMmUKgPpZc/v27TF48GAsXrwYenp6KCwsxL59+/DmzRsMHz4ct27dQk5ODpYtW8aKHwIADg4OOHr0KPz8/HDz5k307t0bQL2ri52dHWbOnIm1a9dCSUkJO3bswOvXr1kahXFxcejdu7dQv9KioiLIy8sL7M8JIzAwEAMGDICZmRkcHR1x+vRppKamIiwsTEDVpiViY2MxatQo5mc1NTUsW7YM8+fPR3V1NYYNG4a6ujqcOXMGoaGh6Ny5c6va/Wz+qVMY0akxG2HPfvDgQZKUkqLBo6fQoh0xFBIeR+6+P5C0jCytW7euybbQIA5JeXk5ycjI0OjRo5n8hqfGt27dou7duxOXy6Xu3btTSkoKTZgwgbhcLr1584auXLlCtra2JC8vT6qqquTj48NIojeXd/LkSdLX1yc5OTlyd3engoICMjExIVtbWyIiysvLo169epG0tDQZGhrSnj17iKh1qjTNKcgUFBRQ//79SUlJidq0aUNubm708OFDpu6mTZtIR0eHZGRkyN7enhWpLzY2lhQVFWnIkCFMv7KysszvpSklHaL6k2v+O+UTHx9PdnZ2JCcnR4qKitS/f39WVDYiotLSUpo1axYZGhoSl8slfX19mjJlCiOfHxwcTFpaWqyIbXz4p8fLli0jNzc3Vt6TJ0/Iw8ODVFRUSFlZmQYMGMCKAUJE1K5dO1q1apVAu0T1CjHBwcEC6cJOjYmIoqOjqVOnTiQpKUnm5uYUHx9PRCSgatOQxqfGVVVVpKioyMjzN+Tw4cNkY2ND0tLSxOPxaMCAAXThwgVas2ZNq8MafA7/uPqMMP6L6jNNKe8kJCRg5cqVSEhIYA4j5s6dy4hzihAh4p9HtDT+yjg5OcHJyQlVVVWoq6tjnTiKECHiy/CPGUJhs75PuTf4pe4afm2+iPe8CBEihCI6Nf4X4ePjw4rCBtQ7L3M4HAGnU39/f2hoaHxyXy2pxAi7Cvctwg9I3zjyHFB/da+hCENRURG8vb2hra0NBQUFdOvWDbt37xYaje748ePgcDisYEiN4auxlJeXY/LkyVBRUYGMjAycnJxYogpFRUVwcXGBkpISDAwMhMZxEYaLiwt27drVqrINqa2thbKyMl68eAF9fX3GZafxp6ioCBEREU3Gtmn8/vj4+/szVyI/lq+lWPOvWRr/2/cV/46Zbe/evXHgwAFWFDH+H3pSUhIT6wKojwTXOAzm3wn/TuzfwcKFC5GWlibg4/glqaqqwoABA2BhYYHjx49DWloaKSkpCAgIwJMnTwTuSkdGRkJCQgLHjh3Dhg0bBIwFX40lMjISgYGBSE5OxrFjxyAvL4+5c+fCw8MD2dnZ4HA4GD58ONq3b4/z58/j2rVrmDRpErS1tTF8+HChY62pqUFUVBTOnDnDUrFJTU1tUq184cTWD58AAB8ESURBVMKFjHFKS0tDx44dmRPwgIAAzJo1S6DOx+pe1tXVISEhAb/++itmzpzJyrt//77QE+CGPqLPnz/H7NmzW61YM3ToUEyYMAFLliyBpKQkkpOTsXDhQuTl5WHHjh0fNfZ/jSH8v8r79+9RUlICZWXlFiPR2dvbM3JJfL+tpKQk5oZJZWUluFwuqqurkZOTI1RX7u9CXl7+oyPnfcukpaWhqKgI2dnZjDO4hYUFKioqEBYWxjKE5eXlOHXqFPz9/bFhwwZcvHhRIHQmX41FVlYWBw8exL59+xj3pvDwcBgaGuLSpUtQUVFBRkYGTpw4AS0tLZibmyMhIQGHDx8Wagjz8/Nhbm4uNIKdtbW1gL9fXl4eJk2axBJNaOwAzePxWBqKn0JFRQVUVFTw/v17ofn5+fkwMjLCkSNHhOZ/bcUa0dL4K/Hy5UtMmTIFqqqq0NPTQ9u2beHt7d2kFBJQ76ispqbGqLxUVVXh0qVLmD17NrhcLi5evAigXkSzqqoKvXv3xvPnzzFixAjweDzweDwMGjQIt2/fZtpsSXGlNSoxycnJkJeXR0xMDHNP1MnJiRXEfOPGjdDQ0ACPx8OcOXMwYMAAREREICQkBMuXL0dKSgrzx/ju3TtMnDgRbdq0gbq6Onx9fZkIevwle0xMDKysrMDlcmFjY8N6pk/h9evXqKysFLjuNXHiRAEVFX6w86CgIKipqQlViuEbm5cvX4LH47F8BPnX9oqLixkfyYY3bWRkZFiO7w3R09PDlStXkJWVJTBjk5WVZanOGBgYICQkhLl10nhsfyf8/39ZWVkCoUGBeiWjrl27ssbX8LbK11asERnCr0BZWRl69+6N/acuQNlpHgx9I6DpsgQnL9+DnZ1dszFxe/XqxRjCS5cuQUJCAj179kTv3r0Zyan09HQoKSnB3NwcgYGBePnyJZKSkpCQkIDa2lpMnToVwF+zhZUrVyI9PR1DhgzB4MGDWWE7ly5dipUrVyItLQ0VFRVC//MB9TPbkJAQ7N69G/Hx8cjNzWX0606ePIlFixZhy5YtSElJwePHj5GYmAgAmDVrFvz9/dGjRw+kpaUBqP+jyMzMxIkTJxAbG4vHjx9jxIgRrP4CAwOZcb19+7bJcbUWe3t7tGnTBubm5pgwYQIOHDiAoqIiqKqqCmwBREZGwtHREW3atMHAgQNx7NgxgX3EuLg4DBkyBOrq6igqKoKBgQGTFx4eDg6HA3Nzc5ibm0NdXR2LFi1CZWUl/vzzT0RGRjZpqLhcLiwtLWFpadlifOH169dDUlKSCYwOAA8fPkRJSUmrQq5+DGJiYsy4hK0UCgoKcO/ePXTt2hVqampwcXFhfXlpaWnB0tIShoaGQtu/ePEiDAwMoK6ujvT0dBgbGwsN0ykhIQEPDw9oaWl93Pg/qrSIv4UtW7bgXkkVtAfOhYyaIcTEJSGt3A5afWfgjaQWc0tDGA11/5KTk2Fvbw8JCQn06dOH2WNLT0/Hd999BzExMdjZ2WHjxo3o2rUrunXrBk9PT+b+amsUV1qrElNXV4etW7eiV69e6N27N0aNGoXr168DANauXYvp06fD09MTFhYW2L17N7PHyePxoKSkBC6XCx0dHTx48ACHDh1CREQEHBwc0K1bN0RERCApKYnV97JlyzBgwABYW1tj0v9r79zDasr+P/4+Fed06ggTXZSE6e5SUykNh4ZJxCQNRuMSMW4zhsHXyLWYMco1Pfz4PtRI5osxX9GJGreGppRKPYr4kuZ0ceuionT5/P7oaU/bOSVjZHTW63n2H3uvtfZnnW37tPb6rPX+zJ7N2fqrdO/eHfHx8ZgwYQJiY2MxdepUGBsbw8HBAcnJyVy9srIy3tzc6NGjIZfLkZiYyNVpTo2lpqYGq1atwrJlyzBnzhyYmZlBLBYjNDQUu3btglgshr29PYyNjV871UJJSQk2btyI7777jjd/KZPJMGrUKN61DRs2KChZ79q1i3c/ZWrXr6LXmJOTg5KSEgQEBOCXX34BAAwfPhxlZWWtav+mFWuYI2wjztZ8iLM1H8J8diTWB4Wis80oCNQUp2i79BuNQ4cONTthPGTIEBQUFCA/Px/nz5/H8OHDATS8VElJSXj69CkvUDJjxgwkJCRg9uzZcHV1xYoVK7jRS2sUV15FJaap8o2Ojg43X5SVlcUTERWLxc1unbp+/TrEYjHvXoaGhujevTsv0tqcrRd52brMpuXm5ubYu3cv5HI5bt26hdDQUJSVlWHcuHHcp+rx48dRXV3NbWVzc3ODuro67/NYmRpLVlYW7O3tsWnTJvj7+3PBv4yMDEydOhUrV65EcnIy99k9efLkFvv9Mnbu3Alzc3MFRWplfZs7dy5PqCI9PR0+Pj68Oi+Wp6env9KoMiwsDFeuXMHYsWPh4uKCI0eOoKqqinOKL+NNK9awYMlboK6qHB20lcs7aUi6oaKiAlVVVUoVWgYOHAiJRIKLFy8iKSkJW7ZsAdAwsS+RSCCTyZCTk4OhQ4eipqYGgwcPhlgsxrRp0zB16lRkZmYiKCgIwJ+KKykpKYiNjcVPP/2E1atXIzExkRuxvYpKTHO5UsrKyhTKmluSUVVVpVS55tmzZ7y+tDYvS2Nk9PHjxwoBgaZKMZs3b4aRkRG3v7tv377o27cvRo4cCTMzM2RlZcHW1pZTijE2Nubd69ixY9iyZQsEAgFkMhlvWc3ly5fh7u4OU1NTJCUl8ebQwsLC4OjoyH0FfPDBB9DS0sJHH33EWx3wKtTV1WHv3r0KwYfq6mrEx8craCHq6uoqqMu8iLLyV3k3lM1n9u7du1XqMsoUa3bv3o2SkhJO5bvp1MNfUaxhI8I24ua+Kdzh9EF/VD1QPrlfdf8WjI2Nm33J1NXV4ezsjO3bt0MkEsHOzg5Ag2ORSqXYvHkzRCIR7O3tkZmZiYyMDJw4cQLz5s3D0KFDOekk4NUVV/4qPXv25H26VlRUNPuJbWZmhtLSUt78UUZGBsrLy2FjY/PKtnv37g09PT0FpZjCwkJkZWVxSjF5eXlKFVAadQfFYjEePXqEc+fOwd/fn6cUExwcjD/++ANXrlxRUGOpr6/HtGnTYG9vr+AEgYaF9C/+URCJRFBXV3/pHGBzXLx4Effv31eIOjfmGGmaIqAtKC8vh56eHjcHDDS8Azk5Oa0SVWgLxRo2InwLzJs3D75ffAVt00HooP0ed72uuhKP045j7dJ5LbYfMmQIVq9ejXHjxnEvB9DwefzVV19h2LBh6NixI/T19aGuro7w8HB4eHggLi4O+/btQ319PeRy+UsVV/4ufHx8sHXrVtjY2KBnz55Yt24dLyoqFApRVFSE27dvw8bGBq6urpg+fTqCg4NRX1+PxYsXw8vLC7169VK6MPpFcnNzoaurC21tbQgEAnzzzTdYv3499PX14eLigry8PHz77bewtrbmAiGNoqO+vr6YO3cuJBIJsrKy8MMPP0AqlcLc3By7d++Gmpoavv76a24kCTQ473Xr1uHo0aMoKCjgqbGkpqbizp072LNnD++PENAgwOrl5YUtW7YgMDAQ48ePx6NHj7B06VJMnDgRIpEItbW1kMvl0NfXb1EMtikxMTEYOHCggsN7E9Hi1iCRSGBvb48FCxZg27Zt6NSpEzZu3Ag9Pb1W9actFGvYiPAt4OPjA59PxyHvxCo8Tj2OintXUZxxCvd+WYnhjtZYunRpi+0b5/8a5wcbaTxvLDc0NMSePXuwdetWODk5cTp3HTt2xPLlyzFq1CisXbsWa9euha2tLQICArBjxw5O7unvYtWqVZg9ezbmzJmDoUOHok+fPhg4cCA3P+fh4YGSkhJOTzEiIgJ6enoYMWIEPDw8YG5u3uyyCmWYmpryZO6XLVuG77//Hps3b0b//v3h4+MDa2trxMXFcZ/hZmZmSElJQXl5Oby8vODg4IB169bB09MT0dHRABpG0J6enjwnCDSMFsePH49jx44pOJvGlAgff/wxTE1NeUdiYiIcHBxw8uRJnD59Gs7Ozvj888/h5OTE5Q2Wy+Vc3daSlJTEfUY25W05QqBhmdbAgQPx6aefYtiwYaiurkZ0dDSXdKo5nj9/jgsXLsDNzY13fenSpYiIiIBMJsOgQYPg6uqK8+fP49SpU5g1a9Yr9++Nqc8oozkFlr+7zbsAEeHEiRPYu3cv7ty5A0NDQ/j6+uKzzz576cvxrpGdnQ0tLS0uu159fT309PQQFRXFSf8zGG+T9vU/7h1CIBDA09NTaaLv9sa///1vREVF4cCBAxCJRAgNDYWOjo7SUQuD8TZgjpDxxgkICEBxcTE8PT3x7NkzODs7Izo6utWRXwbjTcPmCN8hCgsL4efnBwMDA4hEIvTt2xfLli1TyInRHE0Tpb8prly5wqX83L17N0xMTNCtWzfcvn0bZ8+eRWVlJX799VeYm5srbV9bW4suXbooqN7k5ORg2LBhEIvF6NmzJ5YvX66w37ap7TNnzsDW1pZbqL1p0yYFW/Pnz8fWrVsBNCwtCQwMhJmZGYRCIQwMDODj44Ps7OzXfiaMfz5vZUSoKhqDfyf5+flwcnLC+++/j8jISOjr6yMzMxNr1qxBbGwskpOT//Jyi78TmUwGd3d3nDt3Dl9//TV27dqFQYMGYefOnXBzc8OtW7cgkUiUtn3y5AkCAwMVHHt9fT2Xl/n3339HXl4efH19oa2tjTVr1ijYvnPnDjw9PfHNN98gMjISCQkJmDdvHvr06cPbqhcTEwOZTIa6ujp4eHjg9u3b+OGHHzBgwAD88ccf2LJlC5ycnJCcnMzbq8tof7yVYMlfoT0FSyoqKnDhwgXU1tbC2dkZenp6L20zYcIEFBQUID4+nrfgOD8/H71798bevXt5e0qVMWzYMHz44Yevlez7ZTg6OiIoKAjbt2+Hrq4uF+2tqamBkZERNmzYgNmzZyu0279/P/z8/LhdL7du3eL2nSYlJWHw4MGcSg/Q8LkdHR2NpKQkBdsJCQmIjIxEZmYmVzZ16lQUFRVx6S6zsrIwZswY3L17FyEhIVizZg0yMjJ4C6Vra2thb28PW1tbHDhw4G9+Uox/FG88K8pr0p4SPtXX19PatWtJIpFQ945C6iHUpA4dOpCvry9VVlY22+7BgwekpqZGUVFRSsvPnj1L6enpRET0448/krm5OQmFQurduzcFBQVx9aRSKX3xxRf0ySefkKamJllaWtKRI0e48vLycpo5cyaXOGnGjBn0+PFjIvoz6VJUVBTZ2tqSUCgke3t7ysnJ4fWzS5cuVFNTQ1ZWVhQSEsLr56BBgxQSIDXy8OFDSktLo+joaIXkTmfPniUPDw9e/Y0bN1L//v2V2l6+fDnNnz+fV3/x4sVkbW3NnQcFBXF1bGxsaMmSJUr7lZGRwSXKYrRfmCNsQ1auXEm6HTrSd337U5TtEIqyHUKhlh+QmVhCY8aMabadTCYjAPTw4cMW73/jxg3S0NCg8PBwunHjBoWFhZG6ujr99ttvRNTgCAUCAa1fv56uXbtGAQEBJBAIKC0tjYiIpkyZQv3796cLFy5QSkoKjRw5klxdXYnoT0fYp08fOnPmDCUnJ5O5uTl5e3tz9sPDw7nzIUOG0OrVq7my+vp6MjIyInd39xZ/g7Isdy9y+/ZtMjU15WU3a2r7RUpKSsjY2JimTJnCXRs+fDidOnWKKisrSSAQ0NGjR1vsF6N9wxxhG1FcXEyampoUZDaAc4KNR0Q/JxKqqVFiYqLSthEREby0iEQNaRSFQiF3uLq6UlZWFm3fvp2rU19fT/r6+hQWFkZEDY6wMZVlI4MHD6Yvv/yScnNzSSAQUGpqKleWn5/POcpGB3X48GGuPDg4mCwsLLjzSZMmcWk7t23bRoaGhnTt2jWqqqqiwMBAAvDajtDIyIgAkIGBAd27d0+p7aakpqaSpaUlSSQSunnzJhERPXnyhCQSCVVWVpJcLuelRyVqGFU3fbZCoVBpuk1G+4FFjduI06dPoxsJYK6luIm+k0YHOOu8h+PHjytt27hdq6niRkBAAKe2MX36dFRXV8PS0hLOzs7w9/eHt7c3rK2tcf/+fZ5WXmMy9EYcHR1x9+7d11Z9qaurQ1xcHKd2smDBAowePRp2dnbQ1NTE8ePHMWrUKLz33p9bCv8KZ8+ehUwmg5GREcaOHavUNtAQYAkICMCgQYOgpqaGS5cucQGP2NhYToxC2bMdN24c92xDQ0NRXV2tNG8Jo/3AHGEbUVVVBXEL6+Y01TV4+Rua0piwKS0tjbtmYGDAqfw+efIEQINgqFQqRV1dHSZPngyZTKYgUNl0bzLQEMQQi8WvrfqSkJAAExMTTmWkQ4cO2LdvH548eYJ79+4hNTUVxcXFPKXm1pKZmckFRczMzODu7o6QkBBkZGSgsLBQwTYR4dNPP0VgYCBWrFiBq1evcstqAP5Ws86dO8PQ0JD3bHV0dLhnqyyJEKP9wRxhG+Hk5ITbTytQUqOYa6KOCFefFDe73czMzAy2trbYsWOHQllhYSFOnToFAIiMjISvry82bdoEb29v6Orq4vHjx7z6jaKujSQmJqJfv36vrfry4j7WBQsWYPXq1RCLxTA2NoZcLsfVq1cV9PFaw7FjxzBt2jTetefPn0NdXR1du3ZVsB0eHo4TJ07gxIkTCAgIUJBtP336NK/+pEmTcODAAe4PSiN1dXXYv3//K/eX8e7BHGEbYWlpiY8+/hi78m7h+QuiqwcLciHU644JEyY02z4kJAQXL17ElClTcPnyZWRlZSEiIoJbWwgARkZGiI+Px9WrVxEfH4/x48dDQ0MDeXl53GgzNjYWO3fuxPXr17FmzRpkZ2djzpw5PNWXhIQEXLp0CbNmzeJUX17Gi87IxsYGoaGhOHnyJBISEuDl5YWxY8dyqs0VFRWtUpIBgMmTJ+N///sf/vWvfyEjIwNxcXGYN28efHx8IBQKFWwfO3YMbm5usLKyQm5uLncUFRUhLS0NWlpaPEl4f39/TgMwOjoaN2/eRExMDIYPH/7KAp+Md5S3PEf5UtpLsISoYYmHnZ0dddHoQOO6GZK3nhEZCTXJ0NCQMjIyXto+JSWFxowZQxKJhMRiMdnZ2VFYWBilpKSQi4sLFRUV0YgRI0hTU5Osra3p559/pnXr1pFQKKTr16+TVCqlJUuWkJubG4lEIrK0tOQFCQoKCsjT05M0NTVJR0eHfHx8qLi4mIiUBzH27dtHJiYmJJfLqWvXrryAQm1tLX377bfUo0cP6tSpE3322WdUWlrKlR84cICUvX7NBUuio6Np4MCBJBaLycTEhJYsWUIVFRVKbVtZWREAhUMqldKGDRto0aJFCnaLiorIz8+P9PT0qGPHjtS7d29atWoVPX36lDp16sQLVDHaH226oPqv0N7UZxp3Mdy7dw+Ojo6QSqWYPHnySyXlGQzGm4OJLrQx6urqnB5de3HuDMa7DpsjZDAYKg9zhAwGQ+VhjpDBYKg8zBEyGAyV550JljANQwaD8aZgI0IGg6Hy/ONHhO1xiQkb3TIY/yzYiJDBYKg8zBEyGAyVhzlCBoOh8jBHyGAwVB7mCBkMhsrDHCGDwVB5mCNkMBgqD3OE7wiFhYXw8/ODgYEBRCIR+vbti2XLlqG0tLRV7YcNG4ZVq1a90T5euXKFlxsEABYuXKhgt7a2FkuWLIGuri46d+4MNzc3ZGdnK9zPysoKqampePDgASZOnAgdHR1oa2vD09MT9+/fb9F2bGwspFIptLW1oaOjAxcXFxw6dOhv/LWM9gRzhO8A+fn5cHR0xJ07dxAZGYm0tDR89913OHnyJKRSKZ4/V8yD8jaQyWRwd3cH0JBFLi4uDj/++KNCvaCgIBw+fBgHDx7ExYsXoaamhk8++YSXKOnevXsoKSmBra0tPv/8c+Tl5SE2NhZnzpzBnTt3MHPmzGZth4WFwcPDA0OHDsXvv/+O8+fPY9SoUVw+FwZDgbctka2KvGr6AS8vL3JycqLnz5/zrsvlcurYsSOXt7glpFIpLyH6m8DBwYEuXLhAlZWVpKmpyUnkv2jXwsKCtm3bxp3fu3ePAFBWVhZ3LTQ0lGbMmEH5+fkEgFJSUriyX3/9lQCQXC5XsF1UVERisZhCQkIU+hcUFERaWlpUWVn5d/5sRjuAjQjbmEuXLuHcuXOIiorCzJkzcfny5RbrP3z4EP/973+xcuVKhXSbPXr0QExMDJci8+DBg7CwsIBIJEKfPn0QHBzMq//o0SN4enpCLBbDysoKR48e5coqKiowa9YsdOnSBXp6evD19UVxcTEAIDc3FwKBACdPnoSdnR1EIhEcHBx4Ge8ePnyI27dvw8XFBSKRCAkJCUhLS8MHH3yg8JsMDQ0xaNAg7lxDo2GnZ9N0po0JmQoLC9GjRw9eJr1u3boBaJgueNF2REQEtLW1MW/ePAW7c+bMQVhY2D9mBM34B/G2PbEqMX/+fNJUU6fRugY0z6gvuesakKaaOi1cuLDZNjKZjADQw4cPW7z3jRs3SENDg8LDw+nGjRsUFhZG6urq9NtvvxFRw4hQIBDQ+vXr6dq1axQQEEACgYDS0tKIiGjKlCnUv39/unDhAqWkpNDIkSPJ1dWViP5MqNSnTx86c+YMJScnk7m5OXl7e3P2w8PDeeeNvGwk+vTpU/riiy/I1NSUG/E+e/aMOnXqxEv21JSFCxeSSCSisrIyBdsTJ06kMWPGtPisGIwXYY6wjQgLC6MuGh3o/6zsKcp2CHf8n5U9ddboQAcPHlTaLiIiggDwsqj5+fmRUCjkDldXV8rKyqLt27dzderr60lfX5/7bJZKpTR69GjevQcPHkxffvkl5ebmkkAgoNTUVK4sPz+fc5SNjvDw4cNceXBwMFlYWHDnkyZNov379yv0vyVH6O/vTwKBgADQ3r17uesxMTE0dOhQhfrl5eXk5+dHAOj7779XanvEiBHk4+PDa9f0WQmFQjpw4IDS/jBUF/Zp3Ebs2rUL3nrGMBDys9UZCDUxQc8YISEhSttpaWkBAB48eMBdCwgIQHp6OtLT0zF9+nRUV1fD0tISzs7O8Pf3h7e3N6ytrXH//n1QkySFDg4OvHs7Ojri7t27uH79OsRiMWxtbbkyQ0NDdO/eHTk5Ody1puU6Ojp49uwZgIbMfHFxca+cvH3RokVITEzE4sWLMXfuXKSkpAAAoqOjucBHI5cuXUK/fv1w6NAhhISEYMWKFUpta2lp8Z4VAO5Zpaeno1OnTrygDIMBsKhxm5Geno4Bki5KywZIOiM9PV1pmZWVFQAgLS2Nu2ZgYAALCwtYWFjgyZMnAICffvoJUqkUdXV1mDx5MmQyGQwNDXn3UlPj/3PX1NRALBajqqpKYf4RAJ49ewaxWMydq6urK+1jQkICTExMYGBgoLS8KSUlJTh16hSqq6vRrVs3ODo6YuvWrTAzM0NcXBwAICYmRiFhu6urK0xMTJCRkYGFCxc2a9vKygqZmZk8Z9f4rIyNjbl5TwajKcwRthESiQSltcon6Utrn0MikSgtMzMzg62tLXbs2KFQVlhYiFOnTgEAIiMjueUh3t7e0NXVxePHj3n1r1y5wjtPTExEv379YGZmhtLSUl7wIyMjA+Xl5bwgRXM0BjZaQ2lpKcaOHYvU1FTuGhGhpqYG3bt3x82bN1FdXc2tCSwrK8Ps2bMxceJEnDt3Dn379m3R9qRJk1BUVIQjR44o2N6/fz/q6upa1U+GavGPF2ZtL0yaNAmyn46hv6SzQlnMw0JM/nxys21DQkLg6uqKKVOmYMGCBejSpQtSU1Ph7++P999/HwBgZGSE+Ph4XL16FZWVlQgMDISGhgby8vK4aGxsbCx27tyJjz76CP/5z3+QnZ0NmUyG7t27w9XVFdOnT0dwcDDq6+uxePFieHl5oVevXsjNzW3xt8lkMuzevbtVz8HU1BSDBw/GV199hS1btkBHRwd79uxBaWkpxo0bh4iICN5ncVxcHCorK7F8+XLk5eXx7mVkZKRge8CAAViwYAHmzp2LBw8eYOTIkaisrMTx48exf/9+9OrVq1X9ZKgYb3uSUlW4e/cu6erqkruuAR2wcaQo2yF0wNqRRr2nT926daPc3NwW26ekpNCYMWNIIpGQWCwmOzs7CgsLo5SUFHJxcaGioiIaMWIEaWpqkrW1Nf3888+0bt06EgqFdP36dZJKpbRkyRJyc3MjkUhElpaWFBcXx92/oKCAPD09SVNTk3R0dMjHx4eKi4u5vgOgW7ducfX37dtHJiYmJJfLqWvXrlRbW6u038qCJQUFBTRhwgTS1dWlrl270scff0zXrl0jooZgxy+//MLV3bx5M7ce8cXj0qVLSm3X19dTaGgo2djYUMeOHem9996j8ePHU1ZWFi1cuJD27dvXin8xhiohIGoym854o2RnZ2PRokU49+uv0FLXQGVdLT4aORI7d+6Eubn52+4eg6GyMEf4FsjLy4NcLoeRkRF69uz5trvDYKg8zBEyGAyVh0WNGQyGysMcIYPBUHmYI2QwGCoPc4QMBkPlYY6QwWCoPMwRMhgMlYc5QgaDofIwR8hgMFQe5ggZDIbKwxwhg8FQeZgjZDAYKg9zhAwGQ+VhjpDBYKg8/w8GJ4LsmjfSXgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 360x180 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"output_name = \"/Users/lmoncla/Documents/Mumps/paper-and-figure-drafts/eLife-submission-2020-01-08/resubmission-2021-03/figures/individual-PDFs/other-2G-2021-03-05.pdf\"\n",
"width = 5\n",
"height = 2.5\n",
"\n",
"plot_subtree(subtree2,metadata,colors,division_order,output_name, width, height)"
]
},
{
"cell_type": "code",
"execution_count": 437,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAAAqCAYAAAA+nanoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAHa9JREFUeJztnXlYFFfWxt+mxW6aXRYRUSAibVDUEFBBVndQgShqDEZUJC5BlIjRqCw6JFFCgkQzRDEJMi7MYBRFIYMKzeICAVEUUVxQWTSgoqyyyPn+4OsKRTeLGWcyo/V7nnqe7ntv3TpV3XXq1jm33uIREYGDg4ODQy4Kf7YBHBwcHP/NcE6Sg4ODoxs4J8nBwcHRDZyT5ODg4OgGzklycHBwdAPnJDk4ODi6gXOSHHLh8XhQUFDAgwcPZOrCw8PB4/GwaNGi/7xhveDu3bvg8Xi4devWn23KvwSPx0NMTIxMeUhICIyMjJjvjY2N2LRpE0xMTCASiWBqaoqgoCDU1tbKrPvo0SMoKipi1KhRXW43LCwMfn5+AICvv/4aRkZGEAgEGD58OE6ePCl3nf3794PH46G1tbXH/QoPD8eCBQt6bCcPFxcXHDlypFdtf/vtN2hpaeH27dty/w9RUVHg8Xj4+uuvu+2Hc5IcXaKgoIDjx4/LlB87dgx8Pv9PsOi/nwULFrAuHra2tggJCfm3btPX1xfJycmIiopCbm4uvvjiCxw4cADu7u4ybePj4wEABQUFKC4ulttfUlISnJ2dcfDgQQQGBuKLL77AxYsXMXnyZHh4eKC0tJTVvqqqCv7+/j3aSUT49ddfERkZKVMnFArlLlOmTGHaNDY24uzZs5g0aRL27dvXbVsASE5OxqRJk+T+V+Pj4+Hr64vAwECsXbu2W7v79LhnHG8sNjY2OHbsGJYtW8aUPXr0COfPn4eNjc2faBmHlKamJsTGxiIrKwtjx44FAJiZmcHAwADW1ta4d+8eDA0NmfZxcXFYuHAhfv75Z8THx2PTpk2s/mpqapCfnw8nJye4ubnB29sbH3zwAQAgIiICR48eRWxsLGu9VatWwczMDBkZGd3aamxsjHv37smtu3TpEut7Q0MDpk+fDm9vb6YsLS0NFhYWUFNTQ1FRERYsWICAgACmXiQSsfpISkrC9OnTZbZ1+vRpLFiwAL6+vti6dWu3NgPcSJKjG9zd3ZGamsq6bUtMTIS5uTkGDx7MahseHg5jY2MoKSnBzMwMsbGxTN3NmzcxefJkqKqqwtjYGEFBQWhra+uxrqGhAUuXLoWOjg5UVVVhZ2eHnJwcpt+Kigq4ublBJBJBX18fgYGBLJsyMzNhbm4OoVAIGxsb3L17FwAQExMDAwMDVltHR0ds3rwZQPtt2uzZs6GhoQEDAwN8/PHHeP78OdP24MGDMDU1hVAoxJgxY5CWlgYAWLRoEQ4cOIB9+/bB0dERjo6OOHv2LLZs2cKMLmNjYzF06FCoqKjA0tKyR8fSE3V1dWhtbcX9+/dZ5VZWVkhOToampibreGVlZcHDwwOTJk1iRpUdSUlJga2tLYRCIRobGxnHC7Tf/mtpabFCMImJibh06RI2btzYo60nTpxAfn4+Zs6cKVM3bNgw1rJ7925MnToV8+bNY9okJSXBxcUFQPv/Zvz48ax1Ov4nW1tbcfr0aUybNo21ndzcXLz33nuYP38+duzY0aPNAADi4JADADp16hQZGxvTP/7xD6bczc2NgoODydPTk7y8vIiIKCUlhVRVVSkhIYGKioooLCyMeDwe3bt3j4iIxo4dS0uWLKGioiI6efIk6ejo0Pfff99j3caNG8nc3JzOnTtHBQUFNH/+fBoyZAhji7W1Nbm6utLly5cpMTGRNDQ06KeffqKSkhICQMbGxnTixAn69ddfadSoUTR//nwiIvrpp59o4MCBrP11cHCgTZs2ERHRvHnzyMXFha5cuUISiYSGDh1KGzZsICKi1NRU0tXVpfj4eCooKKDg4GASCoV07949qqqqInd3d5o9ezY9ePCAHjx4QJaWlrR69Wqqqqqiq1evUt++fenw4cNUWFhIgYGBJBKJ6PHjx13+Bj/99JNMeXBwMBkaGjLfHR0dqW/fvuTq6ko7d+6kgoICamtrk1nvm2++IVVVVXr+/Dn98MMPBICKi4tZbRYvXky7du2Sa092djbx+Xzas2cPERE9e/aMDAwMKD09ndLS0ggAtbS0yF23I15eXuTp6dll/YULF0hZWZkqKipY5W+99RZdvXqViIhGjBhBM2fOJBMTEzIwMKDly5fTs2fPmLbp6elkaWlJRMT8H06cOEHa2tqkra1NTU1NPdophXOSHHKROsk1a9bQBx98QEREDQ0NJBKJ6OLFiywnefbsWYqJiWHWbWhoIB6PRxKJhIiINDQ0aPv27Ux9eno6nTt3rse6/fv3U3p6OlOXnJxM0ut6RkYGKSoqshxMZGQkhYaGMifF/v37mbodO3bQ22+/TUQ9O8nRo0fTypUrmbq8vDxKSUkhIqKJEydSZGQka93x48fTtm3biIhYx0VaFxwcTERECQkJjEMlImptbaWYmBiqrKwkefTWSVZXV9PmzZtpxIgRBIAA0KBBg2jfvn2s9caMGUNz5swhIqIHDx4Qj8ejL774gqlva2ujAQMG0J07d2S2eejQIVJXVydzc3PGwfj4+NBHH31ERPRKnaStrS2tX7+eVVZUVESDBw9m7FRSUqJZs2aRRCKhxMREMjU1pRkzZjDt169fT0FBQUT0u5NUV1cnS0tL4vF4FBER0aOdUjgn+QZx5swZEqmo0ef7Umnn8QLWMt3Tl959912mrdRJSiQS0tDQoObmZkpISKBBgwYRkawzOHXqFAUEBJCrqysNGTKEAFBaWhoREX311VfE5/PJ3NycPvnkE8Z59lTX3NxMsbGxtGLFCpoyZQrp6OgwTjIqKorMzc3l7qf0pLh8+TJTFh0dzTiWnpzkwYMHSSgUkomJCa1YsYJOnjxJL168ICIibW1tUlRUJIFAwCwKCgrk4+Mj97h0dJK1tbU0fvx4EgqFNG3aNAoPD6f79+93+XsJhcIuneSwYcPkrlNZWUlxcXE0YcIE4vP5lJeXR0REd+7ckblwWFhY0DvvvMN8z83NJbFYzOrv6dOn5OHhQQDIzc2NuShJJBLS19en6upqInp1TlIikZBQKKRHjx6xysPDw2nZsmVE1O4kS0pKmN+EqH2UC4BKSkqIqH2kef78eSL6/f/g5OREDQ0N5OPjQyKRiG7fvt2jrUREXEzyNWVX4hWZ5bPPI2HlNBNqmtoy7R1mzMflgiu4du0aq9zW1hZ8Ph/p6ek4duwY3NzcZNbdvn075syZA1VVVSxZsgQSiYRVHxAQgPv378Pf3x/l5eWYOnUqE/jvrs7V1RVffvklxGIx1q1bhz179jB91tXVQVFRsdtj0DmQ3x0dY47z589HeXk5tm7diufPn+P999/Hhx9+CABoaWlBREQELl26xCyFhYW9SgCoqKggMzMTGRkZsLW1RVxcHMRiMQoKCuS219bWxuPHj2XKq6uroa3d/hsmJiayYrE6OjqYN28eUlJSMHDgQKSnpwNoT9gA7dl3Ho8HHo+HixcvIj8/H7dv3wbAjvkB7Vlracz10KFDSEhIQL9+/QC0J1EePnwIPT09VmZZRUUFu3bt6vFYdMVf//pXeHh4QEtLi1Xe0TYejwcjIyMoKPzuvkaMGAGgPZ5cWlqKhw8fYsyYMaw+oqKioKSkhLCwMKipqcHHxwfUCxE0zkm+QdQ9ewKt/gPl1ikpq0JZVQOVlZWscj6fjxkzZuDIkSM4ceKEXCd54MABfPbZZwgKCoKbmxuampqYusrKSnh4eEBDQwOLFy9GXFwcgoKCkJiY2G3dkydP8MsvvyAmJgarV6/GpEmTWA5DLBajuLgY9fX1TNmqVat6NRWlb9++aGhoYL63tbUxc+hevHiBuXPnoqamBvPnz8ePP/6I77//HomJiQDaEwxVVVVMskAsFiMoKAi5ubk9bvfvf/87vvrqK1hZWWHTpk3IycnB4MGDcfr0abntra2tkZKSwipra2tDWloaM7ugqakJO3bswNOnT1nt+Hw+FBUVmQtFXFwcpk6divz8fGZJTU0Fj8fD4cOHAcg6yXXr1qGmpgZ5eXl4//33Wf37+vqisLCQuVDs3bsXAJCXlwdPT88ej4U8GhoacPz4ccydO5dVXldXh19//RUTJ04E0J5cMjAwQGNjI9Pm4sWL4PP5GDp0KJKSkjB16lSWE5UeEwDQ0NDAzp07kZqaytjdHdwUoNeUa8lRANqvzFJup1oi8+IVue2fVFag7tljmJiYyNS5u7tj3rx5UFJSgoODg0y9gYEBkpOT4ezsjIqKCoSEhKBv3764ffs2rK2tceHCBfj5+WHVqlVoaGhAQkICrKys0K9fvy7rVFVVoaamhkOHDkFNTQ3Z2dn4/PPPAQA3btyAs7Mz9PT0sGzZMqxbtw6XL1/Gnj17kJSU1OOxMTMzQ3V1NU6dOoVJkyYhLCwM1dXVANpPpPv378PPzw9BQUFQUFDAgQMHYGVlBaB95Lt06VKYmZlh2LBh2LNnDyQSCXOcBQIB7ty5g9LSUgwaNAgCgQA3b97Eb7/9BnV1dWzduhUDBgyAlZUVcnNzce/ePVhaWgIAHj58iD59+jCjRH9/f9jZ2WHDhg1YuHAh6uvrERkZiTt37mD58uUA2kfbxsbGcHZ2xpYtW2BoaIi7d+9i3759qKmpwaxZs3D9+nUUFBQgNDQUo0ePZh0LBwcHxMfHY+nSpSgqKoK9vT2Admd89OhRBAQEgIiYmQFAu5PR1tZm7JTaDrRfvPr0aXcrZWVlUFFRgYaGRo+/CdA+Om1ubpb5j506dQrjxo2DsrIyAGDs2LF48eIFFi9ejICAADx79gy+vr7w8vJCv379kJSUxMqKy8PDwwOurq4ICAiAi4sLBg6UP3gAwGW3X1dWrFhBK1asYJVdu3aN+igqkv/2WFY8MvJoPr1r70wzZ85k2uL/Y5JERPX19aSkpETvv/8+U98x9nb9+nUaM2YMCYVCGjNmDKWnp9OSJUtIKBRSTU0NZWdnk7W1NamoqJCOjg4tXLiQnjx5QkTUbd3x48fJyMiIlJWVyd3dnYqLi8nMzIysra2JiKiwsJBsbW1JIBCQiYkJ/fjjj0T0ewzq5s2bjL0dY5JE7XE9DQ0N0tXVpeXLl5OrqysTkywuLqbJkyeTuro6aWpqkpubGyt2+O2335KBgQEpKSmRnZ0dE/cjIkpKSiI1NTVycXFhtisSiZjfIiwsjIyNjUkoFNLQoUNp9+7dzLoODg6seCZR+8wBGxsbUlZWJjU1NZo8eTLl5uay2lRXV9OaNWvIxMSEhEIhGRkZ0fLly5n4XHBwMOnr61Nrayt1RprlDg0NJTc3N6a8srKSSQJ1XqQx1o7Ii0kaGhrKbdtVTDIwMJBJrnVk6dKlMomWy5cv04QJE0gkEpG2tjZ99NFHVFdXR01NTaSmpsaKacr7PxARlZaWkpqaGivhIw8eEadM/jqycuVKAOyRJNA+IXj9ho2wmTobb79jg7qaamQl/wO85lpkZGTIzB/k4HjT4WKSbxj+/v5IPXMKeqJWnD4YgauSw1i2aD7y8vI4B8nBIQcuJvkGYmtrC1tb2z/bDA6O/wn+sJOU3s5xvH4sXLgQubm5rOlAmZmZsLe3R0hICIKDg5lyX19fHD58mAncvyx3796FsbExbt68KTdpFBMTg82bN6OsrOwP9f+fQiKRwMnJCSUlJSyFHqD9kUcjIyNG0aesrAzr16+HRCJBTU0NTE1NsXLlSixZsgQ8Ho+17tGjRzFr1iz4+fnJFYYA2pVxli5dijFjxmDZsmVIS0uDuro6fHx8sGXLFpk+OxMeHo5Lly5h//79L73f0m0fP34c+/btk9vmb3/7G2xtbWFsbIy0tDQ4Ojqy6kNCQhATE8MkhzIzM+Hn54eioiIYGRlh69atMhlvAGhuboaFhQXc3d0RGhrKqlu5ciVMTExQUFCA1tZW1r49fvwYdnZ2aGlpQWZmJvT09LrdR+52m0MGe3t7XL9+HTU1NUyZRCIBj8djnlOWkpOTAzs7u3+bLR4eHrhw4cIr6Wvz5s0yJ+h/mqamJkyZMoXJHmdlZcHLywurV6+WOdGB9qk7ffr0wc8//yx3Tl9HZRxPT080NjYiIyMDe/bswe7du/HVV191aQu9QlUeoD3rXFJSIrO89957vT4+1dXVmDlzJhwcHJCTk4OPP/4Ynp6ecv8DoaGhKCwslNuPdLZFZ+rr6zF9+nTU1tbi9OnTPTpI4F8YSXZOCHD8d/GvjPTt7OyYE0g6Ny0tLQ3u7u5ISkrC8+fPIRQK0dzcjIKCgj+sDdgbVFRUoKKi8m/r/z9NVlYWysrKcPnyZWYy/KhRo9DQ0IDdu3ezJobX19fjxIkT8PX1xY4dO3Du3DmMHz+e1Z9UGaekpAQZGRkoLS1lYssbN25EWFgYPv30U7m2vEpVHqDdsXYeRUupqqrq5qj8TmxsLHR1dREREQEej4eRI0ciNTUVO3fuxLhx45h2V65cQXR0NIYPHy7Th/QO6O2332aVt7S0YPbs2bhz5w4yMzNZ6kjdwY0k30AqKiqwd+9eREVFyb0Si8Vi6OrqMoo7TU1NOH/+PD755BMIhUKcO3cOQPuJ1NTUBHt7e1RWVmLOnDnQ0NCAhoYGpk2bhps3bzJ99qR+0xvFHolEAhUVFSQmJkIsFkMoFGLixImsEzAyMhJ6enrQ0NBAQEAApkyZgpiYGISEhODzzz9Heno6cyLX1dXB29sbmpqa6N+/PxYvXownT54A+F24NzExERYWFhAKhbCysmLt0x/h6dOneP78ucykfW9vb0RHR7PKjh8/jpaWFgQFBUFXV1euao90AnhxcTG0tLRYyTexWIyKigrWHMeOvEpVnldFcXExRo4cyQoRiMVinD17lvn+4sULeHt7Y9u2bay5mt3ZRUTw8vLChQsX8M9//hNisbjXNnFO8g2itbUVK1euhKGhIVZt3Ia1f9kJ85GjMW3aNJnH32xtbRknef78efTp0wfjxo2Dvb09c8udk5MDdXV1jBw5Ev7+/nj8+DHS0tJw5swZtLa2YsWKFQCAwsJC+Pj4YNu2bcjJyYGLiwucnZ0ZhwQAf/nLX7Bt2zZkZWWhoaGhS+mtxsZGhISE4IcffkBKSgquXLmCsLAwAO1OJTAwELt27UJ6ejrKy8uRmpoKAFizZg18fX0xduxYZGVlAQCWLVuG3NxcJCQkICkpCeXl5ZgzZw5re/7+/oxdtbW1vZIE6w47Oztoampi5MiRWLJkCfbv34+ysjLo6Ogwt61S4uLi4OjoCE1NTUydOlXuLXdycjJcXFygpaWFmpoa1uOV0jhuZ4csZcSIERg9ejTzqGFXZGdn48CBA/jyyy/lbvtVoqWlJWNvWVkZqywiIgJqamrw8vKS24c8J7l69WocOnQIwcHBeOedd17OqG5nUXL8zyJvMvmqVatIUX0AGc4OI9OlB8h06QF6y/OvJDIYRePGjWPJa0VERJC+vj4RtU9GdnZ2JqJ2uS1bW1siIvrwww+ZSdO7du2igoICZv2oqCgyMjIiou7Vb15GsUc6YVkqXEBE5OvrS9OmTSMiInt7e0bSjKh9ErympiYjErFp0yZycHAgIqK7d+8Sj8ejixcvMu3Ly8uJx+NRfn4+Y9ehQ4eY+vDw8C6FJaS2SSdwd6TzJPHr16+Tj48PDRw4kJmgbWlpSTk5OUybp0+fkkAgoO+++46I2lV4ADAKSURsZZz6+noaMGAArVu3jhoaGuj69es0dOhQAkDZ2dlybZbyr6rySPvg8Xgs4Q+BQMCoDkmPZ2dxEIFAQHw+n5non5eXR3w+n44cOULNzc30yy+/kEAgICUlJSIiunXrFmlpadGtW7eYYyt9CICIqKamhlRVVam+vp6xS11dnQQCAY0cOZIGDRpENTU13R6PznAjyTeEqqoq7N69G/oT/SDQ/P0RrD5K6tCf6Iec/Cs4c+YMU25nZ4eKigqUl5cjLS0NTk5OAAAnJydkZ2ejoaGBlbRZtGgRzp07Bx8fH0yYMAEbNmxgRj0TJ07Eu+++C7FYDGdnZ+zYsQMTJkyAjo4Osz1zc3Pms7KyMuvZ6s50HAmoq6szz/Beu3aNJRIrEolk4lJSCgsLIRKJWH3p6+tDV1eX9VqDrrbVGSUlpS7t7VwvFouxZ88elJWV4ebNm/juu+/w7NkzuLq6Ms+9HzlyBE1NTXB1dQUATJ06FXw+n3XLffLkSSY5IRKJEB8fj7i4OCgrK2PUqFHMbXRnsYiXIT09Hbm5uVi3bh2rvOO2pVhaWrKEPy5duoSIiAhWm71798q0kT5iCQAWFhbYtWsXFixYAIFAgIULF2LBggXMPvj4+ODTTz/FkCFD5NqbkpICGxsblrhJQ0MD4uPjceTIETx69KjLGG1XcE7yNeVMiy3OtNhC7HMQYp+DGDk3BFDRg6DfYJm2CopCqBhZsV7yNHr0aKiqqiI9PR3Z2dmMkxw1ahRUVVWRlJSE4uJi2Nvbo6WlBTY2NoiJiYGFhQUT/5PSG/Wbl1Hs6er9Os+ePZOp62r6y/Pnz+WqCDU2NrJs6e27fKSxsZ5Ue8LCwnDw4EGmzsTEBCtXrsTJkyfx8OFDJukgVe0ZNGgQeDwe+vXrhxcvXuDw4cPMxafzbeX48eNx//59lJaW4vHjx5g0aRLU1dVhbGzcq32QR29UeaSIRCKZWGbnZ6IHDx4s06ZzXHH58uWoqalBSUkJysvLoaWlxTxznpaWhsDAQCbbnpGRgW3btjFZanl2zZ49GzNnzsSQIUMQHByM3bt3yyhVdQfnJN8UqA08ha4nM/AU+rDedMfn82FtbY0dO3ZAKBTCwsKivR2PBwcHB4SFhUEoFMLS0hJXrlxBQUEBjh07hhUrVsDe3p41b/Jl1W/+KIMHD8bVq1eZ73V1dTJZWimmpqZ4+vQpKxFTUFCA2tpaRnbrZXjrrbfQv39/GdWeBw8e4Nq1a4xqz/379xEVFSWzvlAoBNDuaB49eoTU1FRs2rSJpdoTHh6O0tJS5OTkyCjj3LhxAyYmJqiqqsLAgQOhrKyMhIQETJ48WUYNp7f0VpXnVRIbG4spU6aAz+fD0NCQeRmd9DUMRUVFuHz5MjMKtbS0hI+PDzIzMwEAv/zyi4yT7HihW7t2LUaNGgUfH58u7wo6wz1x85pyI/oD1vd798bjrbei0VJbCUVVXVYdtbWi/l4enJxWsMrt7OwQGBgIV1dX1onm5OQEPz8/ODo6om/fvtDT0wOfz8e+ffswY8YMnDp1CtHR0Whra0NZWVmP6jevCk9PT3zzzTcYMWIEBg8ejJCQEJZsm0AgwMOHD3Hr1i2MGDECEyZMgJeXF8LDw9HW1gZ/f3/MmjULRkZGXWaEO3L37l1oa2tDRUUFPB4Pa9euxZYtW6Cnp8eM6j777DMMHz6cScr4+fnB0tISixcvxvLly6Gqqopr165h+/btcHBwgFgsRlRUFBQUFLBmzRrWKMvU1BQhISGIj49HRUUFSxnH2NgYzc3NWLduHdasWYMzZ84gNjaWcR7Av0+V51ViYWEBb29vREREwMnJCZGRkaitrWXeETRs2DBWe5FIBC0tLQwdOhT5+flQVlaW+1CClD59+iA6Ohrjxo1DYGAgwsPDezbqpSKYHG8UEomEAMgosFy5coUAUGBgIFMWHR1Nenp6pKamRosWLaLCwkIaOHAg816ZrtRvelLskZe46ag00zEZ09zcTJ999hljx9q1a8nS0pLi4uKIiOjixYukq6tLZmZmRERUUVFB7u7upKSkROrq6uTp6ckoEPVGSQhyXq/w7bff0rBhw0ggEJCuri55eXnJvJ7hxo0bNHv2bNLX1yeRSETDhw+n0NBQqqurI6L2ZMTcuXPl/iYffvghGRoaylXGycvLYxSDxGIxHT16lFX/71Tl8fLyYn4HeUiPp1StviOdX0dx8OBBEovFJBKJyM7OjnmvjTw6Jm5CQ0Np9erVvdo3f39/UlBQoAsXLnTZtxROBYjjtaGoqAjKysrMW/Pa2trQv39/HD9+HNbW1n+ydRz/q3AxSY7Xhr1792LixInIyspCbm4uvL29oa6uLiPjz8HxMnAxSY7Xhq1bt+LJkydwd3dHY2MjrK2tcfLkyV5nqDk45MHdbnNwcHB0A3e7zcHBwdENnJPk4ODg6AbOSXJwcHB0A+ckOTg4OLqBc5IcHBwc3cA5SQ4ODo5u+D/Kg60Bx2SqSgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 360x72 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"output_name = \"/Users/lmoncla/Documents/Mumps/paper-and-figure-drafts/eLife-submission-2020-01-08/resubmission-2021-03/figures/individual-PDFs/K-cluster-2021-03-05.pdf\"\n",
"width = 5\n",
"height = 1\n",
"\n",
"plot_subtree(subtree3,metadata,colors,division_order,output_name, width, height)"
]
},
{
"cell_type": "code",
"execution_count": 555,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x72 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"output_name = \"/Users/lmoncla/Documents/Mumps/paper-and-figure-drafts/eLife-submission-2020-01-08/resubmission-2021-03/figures/individual-PDFs/H-cluster-2021-03-05.pdf\"\n",
"width = 5\n",
"height = 1\n",
"\n",
"plot_subtree(subtreeH,metadata,colors,division_order,output_name, width, height)"
]
},
{
"cell_type": "code",
"execution_count": 556,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x72 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"output_name = \"/Users/lmoncla/Documents/Mumps/paper-and-figure-drafts/eLife-submission-2020-01-08/resubmission-2021-03/figures/individual-PDFs/A-cluster-2021-03-05.pdf\"\n",
"width = 5\n",
"height = 1\n",
"\n",
"plot_subtree(subtreeA,metadata,colors,division_order,output_name, width, height)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "LHM-basics (python3)",
"language": "python",
"name": "lhm-basics"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
